Project report 9_1

Spectral Analysis and Filtering of Ambient Noise in Shallow Water
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Abstract
This project aims for spectral analysis of a time series of nonstationary signals such as the
ambient noise in shallow water. Ambient noise may have different non-electrical origins those
are generated by natural, acoustic, or mechanical sources. The deterministic or random signal
contains time independent or time dependent spectral components. It is important to predict
about the future or to facilitate a better understanding of the observed phenomena.
Working with the shallow water is extremely challenging since the environment can
change in both time and space drastically. The unwanted background sound in ocean is called as
ambient noise. It may originate due to natural or man-made sources which vary with location and
frequency. In signal detection application, SONAR (SOund Navigation And Ranging) system
requires knowledge of both, the signal as well as the background within which the signal must be
detected. Therefore the analysis of ambient noise is essential to improve the performance of
SONAR.
The different ambient noise sources are dominant in each of 3-frequency bands (Low,
Medium and High). The PSD (Power Spectral Density) can be used to analyze ambient noise in
the frequency domain. In this project, in order to estimate the power spectrum of ambient noise
the nonparametric estimation methods are used. Efforts in this project are finding out the relation
of ambient noise level with varying wind speed, tide height and temperature at different
frequencies.
Another important issue is denoising of the acoustic signal affected by the ambient noise
in shallow water. The filter is used to reject unwanted signals and to achieve desired spectral
characteristics of a signal. This work is also adds the implementation of adaptive filters instead
of fixed coefficient filters, to filter the ship, whale, dolphin noise from desired signal.

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Contents
Abstract
1. INTRODUCTION
1.1. Motivation
1.2. Problem Statement
1.3. Literature Survey
1.4. Thesis Outlines
2. AMBIENT NOISE
2.1. Introduction
2.2. Standard Measures of Ambient Noise
2.3. Ambient Noise
2.4. Sources of Ambient Noise
2.5. Wenz Curve
3. SPECTRAL ANALYSIS
3.1. Introduction
3.2. Power Spectral Density
3.3. Nonparametric Spectral Estimation
4. ADAPTIVE FILTERING
4.1. Introduction
4.2. Adaptive Filtering Algorithm
4.3. Least Mean Square Algorithm(LMS)
5. RESULTS
5.1 Database
5.2 Spectral Analysis of Ambient Noise
5.3 Adaptive filtering of Ambient Noise
6. CONCLUSION
References
Acknowledgement

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1. INTRODUCTION
The stationarity has always played a major role in signal processing applications. The
statistical techniques for stationary processes, based on the spectral analysis or parametric
models, are well developed and are often employed. In many applications, the assumption of
stationarity fails to be true. For nonstationary time series, the situation is different, mainly for
two reasons. First, there exists no natural generalization of the spectrum from stationary
processes to nonstationary processes, and second, it is often not clear how to set down a
reasonable asymptotic for nonstationary processes. Spectral analysis using Fourier Transform
(FT) is the most commonly used method when one wants to measure the global power-frequency
distribution (power spectrum) of a given signal.
Due to high frequency, the electromagnetic (EM) waves will be spread. Therefore; the
acoustic waves are used as the best medium of underwater signal transmission. The identification
and recognition of acoustic signals has become as the primary issue of underwater techniques.
Since acoustic environment of the ocean is highly variable, the underwater transmission is highly
affected by ambient noise [1]. Ocean ambient noise is the unwanted background noise in the
absence of individual sources.
In an ocean environment, the sources of ambient noise consist of man-made as well as
natural activities. These ambient noise sources includes fishing, commercial shipping, naval
operations, wind-driven waves, ice breaking, earthquakes, bio-acoustic sound generation, rainfall
and thermal agitation of the seawater [2]. Understanding the variability of ambient sound in the
ocean is essential for investigating air–sea interface processes, such as wind, rainfall and
breaking waves as well as for monitoring submarine seismic events and ship traffic.
1. 1 Motivation:
As greater use is made of the underwater environment, a detailed knowledge of
underwater ambient noise is necessary. Ocean ambient noise represents a background noise in
measurements for fisheries, oceanographic or oil exploration purposes. It is also a limiting factor
in the performance of acoustic instruments and in control by acoustic means of research

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instrumentation. The fact that most man-made activities take place in the shallow water areas
corresponding to the continental shelves justifies the need for a detailed description of the
acoustic ambient noise in shallow water. The development and improvement of acoustic
detection and localization systems depends on this knowledge. The more that is known of noise
and signal characteristics, the better detection systems, can exploit their differences to improve
the Signal-to-Noise Ratio (SNR). These include such differences as spectral shape, spatial
distribution and coherence, cross-correlation spectra between the signal and the noise.
In this project, the ambient noise in shallow water is analyzed based on the spectral
contents of it. It reports an emerging consent on identifying the important factors affecting
ambient noise levels. The main areas of possible research on underwater ambient noise related to
its dependence on time and location, its directional distribution, both vertical and horizontal, and
its sources. A good understanding of the mechanisms contributing to ambient noise production
helps us to model and predict the ambient noise characteristics in a given area.
1. 2 Problem Statement:
The importance of underwater ambient noise analysis has progressively increased with a
greater awareness of the potential effects on marine mammals of shipping, boat traffic, military
sonars and seismic surveys, oceanographic experiments and other noise sources. SONAR
systems use spectrum analysis to locate submarines and surface vessels. The performance of a
SONAR system is strongly dependent on the estimation of the detection level and the accepted
probability of false alarm. SONAR performance irrespective of their application is sensitive to
the ambient noise behavior in the region of measurement. Thus, it is important to understand the
ambient noise variability with respect to type of source, propagation characteristics and
environmental fluctuations. It is also important to reconstruct the signal of interest in the
presence of ambient noise. Hence the adaptive algorithm for filtering the specific ambient noise
from desired signal is required to be investigated.
The next session gives literature review which is distributed in three different parts as
review on- ambient noise, spectral estimation of ambient noise and adaptive algorithms for
filtering ambient noise.

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1. 3 Literature Survey:
Peter H. Dahl and authors provided a brief overview and perspective on the subject of
underwater ambient noise, of interest to a diverse set of behavioral, biological and physical
science professionals involved in its analysis. In addition, in this article given an introduction of
the subject to those involved in the analysis of human community noise, and motivates a useful
information exchange [1]. Underwater ambient noise is described in terms of its spectrum, or
frequency content. This is a useful and informative summary of underwater noise.
The major anthropogenic and natural constituents of the spectrum are itemized, and two
spectra, corresponding to nominal high and low ambient noise levels, are introduced to illustrate
the dynamic range of underwater ambient noise. These spectrums are then compared with several
examples of field measurements, and some historical trends in field measurements [2].
Naturally occurring ambient noise in the ocean arises from turbulence and pressure
fluctuations, and from wind-dependent noise such as bubbles, waves and spray from surface
agitation. These sources contribute to the background level of 100 -140 dB re 1 µPa and occur at
frequencies from less than 10 Hz up to 20 kHz. Man-made noise sources such as shipping and
offshore oil exploration and production are so widespread that they are effectively ambient.
Shipping noise is the main source of frequencies below 500 Hz. Underwater source noise from
boats and cruising traffic has been reported in the frequency range 1-5 kHz for motorboats and
lower frequencies for larger vessels [2], [3].
In general over a broad frequency range, the ambient noise spectrum characteristic varies
depending on the sources and conditions existing at the measurement location. The ambient
noise sets the ultimate limit to the minimum sound levels that can be measured. The typical
sound levels of ocean background noises at different frequencies are measured by Wenz (1962).
The summary of spectrum characterization of each component was presented by a curve known
as Wenz curve. It has been concluded that the ambient noise is a composite of different
overlapping components such as turbulent, pressure fluctuation, wind-dependent noise from
bubble as well as surface agitation and ocean traffic. The Wenz curves are plots of the average
ambient noise spectrum for different levels of shipping traffic and sea state conditions according
to the wind speeds. The effects of additional sources including those are irregular and local

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activities are also discussed [3].
The ambient background at some particular location varies with time because of the time
variability of the sources of noise. At a single site, variations of ship traffic and of wind speed
over a time period cause variations of noise level [4]. By Ambient noise, R. J. Urick means the
general, continuous unwanted background of sound at some spot in the ocean. It excludes
momentary and occasional sounds, such as the noise of a ship or an occasional rain storm [4].
The ocean gets noisier day by day because of two basic reasons. There has been a notable
change in the nature of merchant ships. The second one reason is the increase in the offshore oil
activity on the continental shelf of the oceans in some areas. The shipping noise is distinctly
indicated as a separate component from the wind-speed noise. Ambient noise contribution due to
ship is also dependent on the geographical distribution of shipping. Donald Ross has presented
the trends in world merchant shipping. It is also included the important changes in propulsion
plants and in numbers and sizes of ships [5].
The ambient background noise in the sea was ignored during the years prior to World
War II, when fairly sophisticated echo-ranging SONARs were being developed and installed on
ASW vessels. The reasons for this lack of attention are several-fold. First, both the number of
engineers and scientists working in SONARs, as well as the level of funding was extremely
small by postwar standards. Secondly, attention was directed almost exclusively toward echo-
ranging SONARs, in which the ambient background is negligible compared to the background of
reverberation and self noise. Thirdly, no absolute measurements could be made at that time
because standard hydrophones and calibration techniques had not yet become available [6].
During World War II when manpower, money and realization of the value of research
came into being. Also, an added practical incentive for investigation of noise in the sea was the
emergence of the acoustic mine, in which the level of the ambient background must be known in
order to establish the sensitivity requirements for the firing mechanism. Early in the war a
research group was established at San Diego as part of the National Defense Research
Committee Division 6 under the direction of V.0. Knudsen [3]. This group made ambient noise
measurements in a number of days, harbors and offshore areas and wrote a comprehensive report
that was later summarized in the Journal of Marine Research dated 1948. Later in 1954, the

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present author, along with Aubrey Pryce, summarized Ambient noise as part of a more
comprehensive report intended for practical use by engineers and analysts [6].
S. Sakthivel and Murugan have focused on estimating the effects of wind speed on
ambient noise level. It is reported that the wind noise is highly variable in the low frequency
range from 100 Hz to 8 KHz. The research work is also related to the development of a noise
model for analyzing the noise level due to varying wind speeds. The analysis of ambient noise
for varying wind speed is performed for frequencies range of 100 Hz to 8 kHz. It is concluded
that the noise spectrum having a linear relationship with wind speed for the entire frequency
range. It has been observed that the ambient noise level increases with the increase in the wind
speed and it is also observed that the noise level variation decreases as frequency increases [8],
[9].
Recent developments in underwater acoustic modeling have been influenced by changes
in global geopolitics. These changes are evidenced by strategic shifts in military priorities as well
as by efforts to transfer defense technologies to non-defense applications. Paul has discussed the
fundamental processes involved in simulating underwater-acoustic systems and the need of
applying the suitable modeling techniques to simulate the behavior of the acoustic signal in
ocean environments [10]. The strategic shift in emphasis from deep-water to shallow-water naval
operations has focused attention on improving sonar performance in coastal regions. These near-
shore regions, which are sometimes referred to as the littoral zone, are characterized by
complicated and highly variable acoustic environments. Such difficult environments challenge
the abilities of those sonar models intended for use in deep-water scenarios. This situation has
prompted further development of underwater acoustic models suitable for forecasting and
analyzing sonar performance in shallow-water areas.
The performance of marine instruments of underwater acoustic detection or
communication is affected by ambient noise. The type of marine environment has a significant
impact on the underwater sound characteristics. Hence, there must be ambient noise database
which will be useful for research and development of marine acoustic systems. Mandar Chitre
and Authors have developed a database of shallow water ambient noise, and several GUIs were
designed for ease of access the database. Analysis of the collected ambient noise data is done
using power spectral density curves. The observed data is compared with the standard curves

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prepared by others. An observation shows that, in the various shallow water environment, there
exists different ambient noise environments [11].
In the underwater environment, acoustics is one of the prime and important tools to use
for remote cross-examination of the environment. A large array of acoustic tools has been
developed for navigation, sonar and communication. The performance of these tools affected by
the natural and anthropogenic noise of the region in which they are positioned [11]. John Potter
and authors have presented the ambient noise results from several tropical sites and identified
some robust statistical features which are useful for improving acoustic system performance in
the face of this cacophony.
The ambient noise environment is different in deep and shallow waters as well as for
warm and colder waters. It is being dominated for some 80% of the usable bandwidth by
snapping shrimp. In order to optimize the design and operable performance of acoustic systems,
it needs to understand the spatial and temporal statistics of this noise [12], [13]. It also concluded
that some features of ensemble snapping shrimp noise appear to be quite strong, signifying that
knowledge of the properties of ambient noise source can be used to improve performance of
algorithms for ambient noise oceanography.
Matthew W. Legg and authors presented analysis of impulsive biological noise due to
snapping shrimp. Snapping shrimp are commonly found in warm shallow waters, particularly in
reefs or near structures such as piers wharfs and rock walls, or where debris covers the sea floor.
The noise they produce by creating cavitations bubbles with their enlarged claw makes a
significant contribution to underwater acoustic ambient noise which is dominating over
broadband frequency range from 60 Hz to 250 kHz [14]. The suitable techniques for analysis of
point processes is presented and demonstrated how they are applied to real snapping shrimp data.
For some time the classical method of searching for periodicities in time series, i.e. the
periodogram analysis of the series, is useless in many cases. It has been pointed out that some
method of smoothing the periodogram of a series with continuous spectrum is essential as the
classical method gives no convergence to the true spectrum. To overcome the limitations of
periodogram method, M. S. Bartlett proposed method of smoothing the periodogram, making use

9
of the correlogram and a statistical analysis made of the fluctuations in the smoothed
periodogram [16].
To estimate the power spectrum of the signals affected by noise, the parametric and
nonparametric estimation methods. There are various nonparametric methods such the
(modified) periodogram, Bartlett and Welch, and the smoothing approach of Blackman–Tukey
[15]. Soosan Beheshti presented a method for estimating the Mean-Squared error (MSE) of these
PSD estimators to improving the performance of these methods in the MSE sense [17].
A. Das has presented the work carried out on real ambient noise in shallow water in the
presence of heavy shipping activities to study and characterize the distant shipping noise
component for variations due to tide. It has been concluded that the distant shipping noise
component results in ambient noise variations up to 35 dB [18].
Parametric and nonparametric spectral estimation methods assume that the signal is
piecewise stationary while analysis of nonstationary signals. This limitation has been overcome
by M. Abbey using an algorithm based on an adaptive Kalman filter to estimate and to track slow
changes in PSD of nonstationary signals. This method does not assume a piecewise stationary
model of data [19].
The main goal of the ambient noise analysis is to identify predominant noise sources and
their statistical features in a particular region of the ocean. The probability density distribution of
the ambient noise significantly gives clues about the number and diversity of contributing
sources. J Tegowski and authors presented spectral and statistical analysis of ambient noise and
identification of glacier calving events. Ice activity is a predominant contributor to underwater
ambient noise in the Arctic Ocean [20]. The ambient noise generated by glaciers tolerate for the
measurement of melting processes even in the absence of direct observation and can be a good
indicator of rapid climate changes. The detection and analysis of underwater acoustic signals
from a glacier is useful to provide valuable methods to predict the effects of global warming on
the Earth's environment.
Rajesh Kumar, S. Kiruba Veni and V. Natarajan have presented the different spectral
estimation techniques are used for analysis of ambient noise in shallow water. This analysis is
specially related to the wind driven ambient noise using different nonparametric methods[ 21].

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They have also developed the adaptive algorithm like LMS (Least Mean Square), modified
LMS, RLS (Recursive Least Square) etc. for filtering ambient noise. Various adaptive algorithms
are processed and compared to achieving maximum SNR. The signal of 7 kHz is taken as the
signal of interest, which has to be reconstructed using an adaptive algorithm in the presence of
noise. It is observed that the implemented adaptive algorithm is able to reconstruct the signal of
interest [22].
1. 4 Thesis Outline:
This thesis is divided into six chapters. The chapter 1 is an introduction describing the
motivation and objectives of the study. The available literature on ambient noise, spectral
estimation of nonstationary signals and adaptive filtering in the past, present and to the best of
knowledge are also reported. Chapter 2 gives the brief introduction to the various sources of
ambient noise in the ocean. The nonparametric methods of estimation of PSD used for ambient
noise analysis are described in chapter 3. The chapter 4 describes the experimental methods and
some adaptive algorithms for filtering ambient noise. Chapter 5 reports the results of ambient
noise analysis in various conditions with different parameters. Also, some results are compared
with standard research. Chapter 6 is a concluding remark on project work. This chapter
summarizes the results and draws some conclusions based on these results.

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2. AMBIENT NOISE
2.1 Introduction:
Underwater signal transmission uses the acoustic signal of low frequency range. The
transmission of electromagnetic waves is impossible due to its high attenuation nature. Typical
frequencies associated with underwater acoustics are in between 10 Hz and 1 MHz. Frequencies
lower than 10Hz can penetrate deep into seabed and frequencies above 1 MHz absorbed quickly.
Hence low frequency acoustic signal is more suited for transmission in underwater. Both marine
animals and people use sound as a tool for finding objects, navigating, and communicating under
water. Acoustic waves travel far greater distances than that of the light under water. Light in the
ocean gets absorbed or scattered after traveling few hundred metres of distance. When light is
available, it is more difficult to see as far under water as in air, limiting vision in the marine
environment. Underwater sound allows marine animals to gather information and communicate
at great distances and from all directions. Many marine animals rely on sound for survival and
depend on adaptations that enable to sense the surroundings acoustically, protect themselves,
communicate and locate food under water [2], [3].
The ocean is filled with sound or ambient noise. Ocean ambient noise is an inherent
characteristic of the medium having no specific point source. Underwater sound is generated by
different natural sources including breaking waves, rain and marine life. It is also generated by
different of man-made sources, such as ships and military sonars. Some sounds are present more
or less everywhere in the ocean all of the time. This background sound in the ocean is called
ambient noise. Ambient noise is the prevailing, unwanted background of sound at a particular
location in the ocean at a given time of the year.
The ambient noise is also defined as the noise associated with the background
disturbance originating from a many of unidentified sources. Its distinguishing features are that
due to multiple sources individual sources are not identified i.e. the type of noise source as
shipping, wind etc. and no one source dominates the received field [1]. Ambient noise is the
noise that is the typical or persistent noise background at some spot that is independent of the
means used to observe it. That means the existing, sustained unwanted background of sound at
some spot in the ocean is ambient noise [3].

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2.2 Standard Measures of Ambient Noise:
A precise definition of the underwater acoustic noise level (NL) is the average acoustic
intensity level measured by a hydrophone within a specific frequency band. Ambient noise is
commonly quantified by its noise spectrum level (NSL) as a function of frequency. The
reference level used to calculate the decibel level of sound wave is dependent on the type of
material used to travel that sound wave. It is generally expressed in decibels (dB), with reference
to a plane wave of 1 µPa rms pressure. It is also referred as the sound pressure level (SPL). The
logarithmic unit decibel is used to describe the strength of acoustic fields, in terms of a base 10
scale. In acoustics, the corresponding units are acoustic intensifying (power per unit area) and
(SPL, force per unit area), and in decibels these are defined as follows:
‫ݕݐ݅ݏ݊݁ݐ݊ܫ‬ ‫݈݁ݒ݁ܮ‬ሺ݅݊ ݀‫ܤ‬ሻ = ܵ‫ܮܫ‬ሺ݅݊ ݀‫ܤ‬ሻ = 10 logଵ଴ ൬
ூ
ூೝ೐೑
൰ (1)
ܵܲ‫ܮ‬ሺ݅݊ ݀‫ܤ‬ሻ = 10 logଵ଴ ൬
௣
௣ೝ೐೑
൰
ଶ
= 20 logଵ଴ ൬
௣
௣ೝ೐೑
൰ (2)
It defines SPL in terms of the square of the pressure amplitude, and to emphasize the all-
important reference pressure level, i.e. “݀‫ܤ‬ ‫݁ݎ‬ 20 µܲܽଶ
”” is required if measurements of p were
made in the air and “݀‫ܤ‬ ‫݁ݎ‬ 1 µܲܽଶ
”” if they were made in water. It can also express the
reference levels as “݀‫ܤ‬ ‫݁ݎ‬ 20 µܲܽ”” in the air and “݀‫ܤ‬ ‫݁ݎ‬ 1 µܲܽ”” in water as in eq. (2).
Ambient noise comprises of a number of components that contribute to the NL in varying
degrees depending on the location of the measurements. Hence, measurement and
characterization of ambient noise form a significant part in any underwater activity. The
underwater ambient noise level is depending both on the strength and density of sources of
sound. This function describes the approximate magnitude range for the pressure spectral density
of ambient noise under water. To obtain SPL or NL in dB from spectral level values, a particular
bandwidth of interest needs to be identified if the spectral level where a constant N, over the
bandwidth B, in Hz,
ܰ‫ܮ‬ = ܰ + 10 logଵ଴ ‫ܤ‬ (3)
For underwater acoustics, the decibel unit for pressure spectral density is “݀‫ܤ‬ ‫݁ݎ‬ 1 µܲܽଶ
/‫,”ݖܪ‬
which is called the spectral level.

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2.3 Ambient Noise
Ambient noise in the ocean is the sound field against which signals must be detected. In
the ambient noise field, it is often difficult to ascribe a particular sound to a localized source.
Ocean ambient noise originates from both anthropogenic and natural sources. Different noise
sources are dominant in different frequency bands namely; low frequency band associated with
frequencies in range 10 to 500 Hz, medium frequency band from 500 Hz to 25 kHz and
frequencies above 25 KHz is high frequency band. The process including wind, wind-driven
waves, rainfall, bio-acoustic sound generation, earthquakes, and thermal agitation of the seawater
is categorized as the natural sources of ambient noise. Anthropogenic noise is generated by
various activities, including commercial shipping; oil and gas exploration, production, and
development like air-guns, ships, oil drilling; naval operations e.g. military sonars,
communications, and explosions; commercial or civilian fishing sonars, acoustic deterrent, and
harassment devices; research and other activities such as construction, icebreaking, and
recreational boating.
Underwater ambient noise is any element or sound that tends to interfere, with our ability
to reliably transmit data. Multiple human and natural factors contribute to the generation of noise
in shallow water [7]. The underwater ambient noise environment depends both on the strength
and density of sources of sound and on the propagation depends on the particular underwater
environment as set by sound speed and acoustic properties of the ocean water and seabed. There
must be the large fluctuations in the level of underwater ambient noise upon a change in time,
location, or depth.
2.4 Sources of Ambient Noise
The ocean, as a propagation medium is full of interfering noise sources such as
machinery noise from the shipping traffic, flow noise, wave noise, wind noise, noise from
biologics and even intentional jammers, which may interfere with the desired target returns and
emissions. The ocean environment includes a variety of noise sources, which are either of natural
or manmade in origin. The general background noise has contributions from all the oceanic noise
sources is termed as the ambient noise. The ambient noise has a broad frequency range, and its
characteristics depend on a number of factors including climate, wind speed, presence of aquatic

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organisms, etc. The following sections briefly examine the principal sources of ambient noises
and their characteristics.
Fig. 2.1 Conceptual diagram of Sources of Ambient Noise in the sea
2.4.1 Natural Sources:
The natural sources of ambient noise can be broadly classified into the following
categories:
• Hydrodynamic sources
• Thermal agitations
• Seismic sources
• Biological sources
• Ice cracking
a. Hydrodynamic sources:
Hydrodynamic sources include a large number of sources which generate noise due to
various physical phenomena, including movement of water itself due to winds, tides, currents
etc. Surface waves are a predominant source of hydrodynamic noise and originate mainly due to
wind action and contribute to the low frequency noise spectrum. The bubbles are yet another
source from which hydrodynamic noise originates. Another source, namely, turbulence is
commonly formed in the ocean in regions which are near to coastal areas, straits and harbors.
Turbulence can occur at the water-ocean floor boundary at the sea surface as well as within the
water.

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b. Seismic sources:
Various types of disturbances in the earth's crust (tectonic as well as volcanic actions) can
also contribute to the ambient noise in the sea. Even if the sources of these types of disturbances
are far away from the sea, those high energy disturbances easily reach the oceans as compression
waves. The spectral characteristics of such noises depend on the magnitude and range of the
seismic activity, the propagation path, etc. It has been reported that, the spectral peaks due to the
seismic activities occur between 2 and 20 Hz, when the disturbances are waterborne.
c. Thermal agitations:
The effects of thermal agitations of the medium determine a minimum noise level for that
medium. The minimum ambient noise level at upper frequency limits of ambient noise data,
around 20 to 30 kHz, are mainly contributed by thermal agitations.
d. Biological Sources:
The ocean serves as a habitat for millions of life forms. A large variety of marine
organisms like crustaceans, mammals, fishes etc. are good noise makers. Noise from such
sources exhibits a wide frequency spectrum from 10Hz to 100 kHz. The individual sounds are
repetitive in nature and of short duration. Generally, noise from various sources blend to form a
bewildering mix of noise.
e. Cracking of Ice
Shifting and breaking of ice is a prominent source of noise in the ocean, especially in the
Polar Regions. The noise originates from cracking, grinding, sliding and crunching of icebergs
dominates wide range of frequencies.
f. Other Sources
Spray of water droplets and hail constitute precipitation. It generally contributes to the
ambient noise at the frequency above 500 Hz. At low wind speeds, heavy precipitation can
generate noise around 100 Hz. Rain also contributes to the increase in ambient noise levels.
Heavy rains are found to cause an increase of about 30dB in the 5 to10kHz range of the noise
spectrum.

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g. Wind Noise:
In the absence of sound from ships and marine life, underwater ambient noise levels are
dependent mainly on wind speeds at frequencies between 100 Hz and 25 KHz. Different
processes are dominant in different portions of the overall frequency band from 1 to 50 KHz. In
shallow water, in the absence of local shipping and biological noise, wind noise dominates the
noise of distant shipping over the entire frequency. Ocean sound in the band of 500-20,000 Hz
has been called wind noise or Knudsen noise because Knudsen discovered that it correlated very
well with wind speed(Knudsen et.al 1948). Dietz, Kahn, and Birch reported that in all frequency
bands, at low wind speeds, the average noise level was independent of wind speed and at high
wind speeds; the noise level was linearly related with the logarithm of the wind speed [4].
2.4.2 Anthropogenic Sources:
Anthropogenic noise in the ocean comes from a variety of sources, some of which make
sound intentionally, and others produce sound as an unintended byproduct of other activities.
Among those sources that produce sound intentionally are explosions, seismic exploration,
sonars, and acoustic deterrent devices. Sources where sound is an unintended by-product include
shipping and industrial activities.
a. Ocean Traffic:
The ocean traffic generated ambient noise may include significant contributions from two
types of noise ships and traffic noise. Ship noise is the noise from one or more ships at close
range. It may be identified by short-term variations in the ambient-noise characteristics such as
the temporary appearance of narrow-band components and a comparatively rapid rise and fall in
noise level. Ship noise is usually obvious and generally can be and is deleted from ambient noise
data. Traffic noise is noise resulting from the combined effect of all ship traffic.
Ship Noise:
Shipping traffic has been found to be a dominant noise source in the oceans. It has been
found that the non-wind dependent component of the frequency range from 10Hz to 1000 Hz is
contributed mainly by ship traffic noise. The effect is predominant at frequencies between 20 and
500 Hz. The ambient noise depends on the particular combination of transmission loss, number

17
of ships, and the distribution of ships pertaining to a given situation. It has been found that the
effective detectable range for shipping noise in the open ocean can be as high as 1000 miles or
more.
The majority of the noise power radiated into the water by surface ships comes from
propeller cavitations. Underwater ambient sound from commercial shipping is typically quite
variable given that this contribution is strongly modulated by both shipping activity and
environmental conditions for long range acoustics propagation [1].
b. Seismic surveys
Seismic reviews are carried out in order to study the structure and composition of
geological formation of earth's crust and to detect the presence of natural resources like
hydrocarbon reservoirs. Such surveys are carried out by directing high intensity, low frequency
sound signals through the earth's crust. The reflected signal is processed to get the required
information.
c. Oil and gas exploration / production
Ocean beds have a rich source of oil and natural gases. Noise is generated during all
phases of oil and gas production, including exploration, pile driving, pipe laying, drilling and
platform operations. The noise thus generated may be impulsive or continuous.
d. Military operations
With newer technologies and innovations which are being developed in the Defense
Research and Development Programs, military operations are now-a-days becoming a major
source of underwater noise. Naval forces may conduct various experiments and test fire their
equipments/gadgets, etc., which could significantly disturb the ocean environment and contribute
to short term changes in the ambient noise levels.
SONAR:
SONAR is a technique that uses sound propagation usually to communicate with,
navigate or detect objects on the water surface or underwater. There are two types of technology
related with SONAR; passive sonar and active sonar. Passive sonar is used for listening the
sound made by vessels, whereas active sonar is emitting pulses of sounds and listening for

18
echoes. The use of active sonars also contributes to ocean noise. Sonar systems generally emit
short pulses of sound with high energy. Moreover, the explosives used in military tests and
exercises can be a considerable source of undesirable noise with typical source levels of 267dB
in the frequency band from 1 to 7 kHz [4].
a. Wenz Curve:
Ambient noise is generally broadband in nature. The ambient noise level varies in
spectral behavior depending on the type of sources and environmental conditions. The summary
of spectrum characterization of each component was presented by a curve known as Wenz curve.
It has been concluded that, the ambient noise is a combination of at least three components-
turbulent-pressure fluctuation, wind-dependent noise from bubble as well as surface agitation
and ocean traffic. The typical sound levels of underwater ambient noises at different frequencies
are measured by Wenz (1962) known as Wenz curves. Hence the plots of the average ambient
noise levels for different density of shipping traffic, and sea state conditions according to the
wind speeds. The effects of additional sources including those are irregular, and local activities
are also discussed. In this graph, the sound levels in underwater are given in logarithmic unit dB
re 1 µPa2/Hz.
The sources of ambient noise can be categorized by the frequency of the sound. The
frequency range of 20-500 Hz is associated with the ambient noise generated by distant shipping.
The distant ships can be detected in the absence of other nearby noise sources. The ambient noise
level increases the particular region with heavy shipping traffic.
In the frequency range of 500-100 KHz, ambient noise is generated due to bubbles and
sprays associated with breaking waves and the noise level increases with increasing wind speed.
At high frequencies above the 100 KHz, the dominating ambient noise is due to the random
motion of water molecules, known as thermal noise.

19
Fig. 2.2 Wenz curve

20
From the Wenz curve shown in Fig. 2.2, the noise levels depend heavily on shipping
density and industrial activities in the frequency band 10Hz-100Hz. Levels are typically in the
range of 60-90 dB with very little frequency dependence. Noise in the band of frequency 100
Hz-1 kHz is dominated by shipping with decreasing intensity as frequency increases. A
significant contribution is also from the sea surface agitation. It becomes dominant up to the
frequency of 100 kHz, unless marine mammals or rain is present. After this frequency range, the
noise is dominated by electronic thermal noise.

21
3. SPECTRAL ANALYSIS OF AMBIENT NOISE
3.1 Introduction:
Estimation of the power spectrum can be defined as the method of finding strength of
hidden frequency contents in the harmonics of a measured signal in the presence of noise. The
ambient noise masks the acoustic signals from underwater instruments. Hence the detection and
cancellation of background noise is essential to improve the SNR of underwater acoustic
instruments. Also, the knowledge of ambient noise sources in terms of frequency is also the
important factor. For that purpose first need is to extract the features of the acoustic signal
affected by ambient noise. There are signal processing techniques classified as signal analysis
and the signal filtering.
a. Signal Analysis:
The primary goal is to extract useful information or features that can be used to
understand the process of signal generation or for signal classification purpose respectively. This
type of methods is known as spectral estimation and signal modeling. In this project, the ambient
noise analysis is done using spectral estimation method.
In the case of a deterministic signal composed of sinusoidal components, a Fourier
analysis of the signal can be carried out by taking the DFT (Discrete Fourier Transform) of a
finite length segment of the signal obtained by appropriate windowing, provided the parameters
characterizing the components are time-invariant and independent of the window length. On the
other hand, the Fourier analysis of nonstationary signals with time-varying parameters is best
carried out using the STFT (Short Time Fourier Transform).
In many practical solutions, to develop a suitable spectral estimation method from
available samples of noisy signals to find the hidden frequency components in the harmonics of
the noisy signals. Neither the DFT nor the STFT is applicable for the spectral analysis of
naturally occurring random signals as here the spectral parameters are also random. These types
of signals are usually classified as noise like random signals. Spectral analysis of a noise like
random signal is usually carried out by estimating the power density spectrum using Fourier-
analysis-based methods. There are many methods of spectrum estimation have been proposed

22
and developed. The nonparametric or classical methods, which are based on estimating the
autocorrelation sequence of a random process from a set of measured data and taking the Fourier
Transform to find the estimate of the power spectrum. The signal plus noise random signal can
be analyzed using parametric methods in which the auto-covariance sequence is first estimated
from the model, and then the Fourier transform of the estimate is evaluated.
In this chapter, the nonparametric methods of spectral estimation are reviewed. Also, the
application of spectral estimation in ambient noise analysis is discussed in detail.
b. Signal Filtering:
The primary goal of the signal filtering is to improve the characteristic of a signal up to
an acceptable criterion of performance. The filtering of the signal can be subdivided into the
areas of frequency selective filtering, adaptive filtering and array processing. The best option is
to select proper adaptive filtering algorithm. The brief discussion about adaptive filtering is
reviewed in the next chapter.
3.2 Power Spectral Density:
The spectrum can be described by nonparametric or parametric methods. The Spectrum is
represented by the phase and amplitude by the squared magnitude known as the Power
Spectrum. In other words, any process that quantifies the strength of the signal at different
frequencies referred as a spectral analysis method.
Now days, spectrum estimation plays a major role in many science applications such as
radar, sonar, speech processing, biomedical signal processing, seismology, vibration analysis,
econometrics and underwater acoustics. Spectral estimation plays an important role in signal
detection and tracking. The applications of spectral estimation include Spectral smoothing, time
series extrapolation and prediction, harmonic analysis and prediction, bandwidth compression,
beam forming and direction finding.
The ambient noise in shallow water has multiple frequencies and intensities that change
over time. Spectral estimation is based on first estimating the autocorrelation sequence from a set
of measured data from a random process, and taking the Fourier transform to find the estimate of

23
the power spectrum. These include nonparametric methods based on Fourier transform and time
frequency analysis such as Wavelet. In practical application, where only a finite segment of a
signal is available, we cannot obtain the complete description of the adopted signal model (true
or theoretical spectrum). The quality of the estimated spectrum is depending on,
1. How well the assumed signal model represents the data.
2. What values are assigned to the unavailable signal samples.
3. Which spectrum estimation method we use.
3.3 Nonparametric Spectrum Estimation
When the method for PSD estimation is not based on any assumptions about the
generation of the observed samples other than wide-sense stationarity, then it is termed a
nonparametric estimator. The Power spectral density (PSD) function shows the strength of the
variations (energy or power) as a function of frequency. PSD shows that, at which frequency
ranges variations are strong, and that can be useful for signal analysis or where the average
power is distributed as a function of frequency [6]. Here, three nonparametric spectral estimation
methods are used, namely Periodogram, Bartlett, Welch and Blackman Tuckey Method.
3.3.1 Periodogram Method:
It is based on the Fourier Transform of estimated autocorrelation sequence of a random
process. A simple method of estimating PSD is called the Periodogram Method.
Algorithm:
• Step1: This estimation method assumes the observed signal is: signal ][nxN a truncated
version of the infinite time series ][nx :
Where, w(n) = 1 0 ≤ n ≤ N-1
= 0 otherwise
x[n]w[n][n]x N =

24
• Step2: The autocorrelation is computed by taking the average over the finite interval as
follows:
…… 0 ≤ n ≤ N-1
• Step 3: Taking the DTFT, the power spectrum estimated using the Periodogram Method is:
It is convenient to write the periodogram directly in terms of the observed samples
x[n]. It is then defined as,
Thus, the periodogram is proportional to the squared magnitude of the DTFT of the
observed data. The above definition of the periodogram stems from Parseval’s relation on the
power of the signal. The window w(n), which is known as the data window of length N. A few
samples of are used in the computation power spectrum, therefore, yielding a poor estimate of
the true correlation. This results as rapid amplitude variations, in the periodogram estimate. A
smoother power spectrum estimate can be obtained by the periodogram averaging method. The
performance of a PSD estimator is evaluated by several measures of goodness.
a. Bias of Estimator: Bias of estimator is defined as the expected difference between estimated
and true PSD. It is given by,
Where, )( fP and )(ˆ fP are the true and estimated PSD respectively. If the bias b(f ) is
identically equal to zero for all f the estimator is said to be unbiased, which means that on
average it yields the true PSD.
b. Variance: Among the unbiased estimators, we search for the one that has minimal
variability. The variability is measured by the variance of the estimator
21
0
2
per ][
1
(f)Pˆ
∑
−
=
−
=
N
n
fnj
enx
N
π
][][*
1
[k]ˆ
1
0
knxnx
N
kN
n
+= ∑
−−
=
γ
∑
−
+−=
−
=
1
1
2
per ][ˆ(f)Pˆ N
Nk
fkj
ek π
γ
)](ˆ)([b(f) fPfPE −=
))])(ˆ()(ˆ([v(f) fPEfPE −=

25
c. Resolution: Another important metric for comparison is the resolution of the PSD
estimators. Resolution is defined as the ability to specify spectral features and is used to
analyze the performance of spectral estimator. It corresponds to the ability of the estimator to
provide the fine details of the PSD of the random process. For example if the PSD of the
random process has two peaks at frequencies f1 and f2, then the resolution of the estimator
would be measured by the minimum separation of f1 and f2 for which the estimator still
reproduces two peaks at f1 and f2.
3.3.2 Bartlett Method: (Periodogram Averaging)
Bartlett has proposed method for estimation of PSD using periodogram averaging. Hence
this method is also known as Bartlett. In this method, the input sequence x(n) of length N is
partitioned into K non-overlapping sequences of length L such that N=KL. The Bartlett estimate
is given by the following algorithm.
Algorithm:
• Step1: The signal is split up into overlapping segments. The original data sequence ][nx is
split up into K nonoverlapping sequences such that each data sequence of length L. Then the
ith
segment is denoted by
{ } 1
0
][
−L
i nx Ki ,.....,2,1.......... =
• Step2: Take the Fourier transform of each segment
• Step3: The squared magnitude of the result is then divided by Length of each segment
• Step4: Average the result of the periodogram for the K data segments
3.3.3 Welch Method:
Welch has improved method of estimating PSD by modifying the Bartlett method. The
method uses the finite segments of time series data with allowing possible overlapping.
Algorithm:
21
0
1
0
2
B ][
1
(f)Pˆ
∑ ∑
−
=
−
=
−
=
K
i
L
n
fnj
i enx
KL
π

26
• Step1: The signal is split up into overlapping segments: Let the length of the segments be
L, the ith
segment be denoted again by { } 1
0
][
−L
i nx and the offset of successive sequences
by D samples.
Then ith segment is given by,
…… 0 ≤ n ≤ N-1
where, i =1,2, _ _ _K
• Step2: Multiply nonrectangular window function to individual overlapping sequence
• Step3: Compute the periodogram of each sequence by,
• Step4: The Welch spectrum estimate is then given by,
The averaging of modified periodogram reduces the variance of the estimate as compared
to an estimation of single periodogram of the entire time series. By permitting overlap of
sequences, we can form more segments than in the case of Bartlett’s method. Also, if we keep
the same number of segments, the overlap allows for longer segments. The variance of estimator
gets reduced by the increasing number of segments, and the longer segments improve its
resolution. Thus, with the Welch method we can trade reduction in variance for improvement in
resolution in many more ways than with the Bartlett method.
These three methods are evaluated by experiment on simulated signal with adding noise.
The simple sine signal is generated in Matlab, which contains three frequencies 500Hz, 1 kHz
and 1.5 kHz. Then the white Gaussian noise added with the original signal as shown in figure
3.1(a). The PSD of this noisy signal is computed using the nonparametric estimation methods
21
0
2(i)
M ][][
1
(f)Pˆ ∑
−
=
−
=
L
n
fnj
i enxnw
L
π
∑ =
=
K
i
K 1
(i)
MW (f)Pˆ1
(f)Pˆ
],)1([[n]x i Dinx −+=
)1( −+= KDLN

27
namely, Periodogram, Bartlett and Welch. The spectrum using these nonparametric estimators is
as shown in figure 3.1(b), (c) and (d) respectively.
From figure 3.1(a) it is observed that the spectrum shows three high peaks at frequencies
500Hz, 1 kHz and 1.5 kHz. The spectrum shows a main lobe and several side lobes. These side
lobes account for the effect known as spectral leakage. The use of a rectangular window for the
implementation of the periodogram results into the high variance and low resolution of PSD.
Hence the periodogram is poor estimator of PSD. The variance of PSD estimator comparison is
listed in Table 3.1.
Fig. 3.1 Spectrum of simulated signal using nonparametric methods
(a) Noisy signal (b) PSD using Periodogram (c) PSD using Bartlett (d) PSD using Welch
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
-4
-3
-2
-1
0
1
2
3
4
Time
(a)
Amplitude
Noisy signal
0 500 1000 1500 2000 2500
-160
-140
-120
-100
-80
-60
frequency(Hz)
(b)
Power(dB)
PSE
0 500 1000 1500 2000 2500
-120
-100
-80
-60
-40
-20
frequency(Hz)
(c)
Power(dB)
BSE
0 500 1000 1500 2000 2500
-100
-90
-80
-70
-60
-50
-40
-30
-20
frequency(Hz)
(d)
Power(dB)
WSE

28
Table 3.1
Comparison PSD method based on Variance
Sr. No. Method of PSD Variance
1. Periodogram 148.8774
2. Bartlett 86.2486
3. Welch 71.9363
There are some frequently used nonrectangular windows as an alternative to the
rectangular window to improve the performance of PSD estimator. Some examples of
nonrectangular windows are Bartlett, Hanning, Hamming, and Blackman window. The
magnitude of the DTFT of a window provides two important characteristics about it. One is the
width of the window's main lobe, and the other is the strength of its sidelobes. A narrow main
lobe allows for a better resolution, and low sidelobes improve the smoothing of the estimated
spectrum.
These improvements are shown by the results by averaging periodogram of segmented
data sequence. The spectrum by using Bartlett method of PSD estimation is as shown in Figure
3.1(c). It shows relatively high and narrower peaks at the expected frequencies as compared with
that of the periodogram method. The spectrum shows the reduction in effect of spectral leakage
as well as the reduction in variance as compared to that of the periodogram method.
The figure 3.1 (d) shows the better spectrum of as compared to the earlier two, which is
estimated by using the modified averaging periodogram i.e. Welch method. This method
averages the periodogram of overlapping segments, weighted by the nonrectangular window. For
this example, 50% overlapping is allowed. The estimator is evaluated by performance parameters
such as the variance and the resolution of PSD. Welch method reduces the variance as well as the
effect of spectral leakage.
In this chapter, the need of extraction of the spectral characteristics of deterministic and
random signal is discussed. For the nonstationary signal analysis, the different nonparametric

29
methods of PSD estimation are available. The review of the algorithms of some nonparametric
methods is presented in this chapter. For the analysis of ambient noise data, the Bartlett and
Welch methods are used. These methods are firstly experimented on the simple simulated
sinusoidal signal of mixed frequencies and then applied for the ambient noise data. The results
for ambient noise analysis using these methods are discussed in the chapter 5. Another important
task in the project work is the filtering of ambient noise from the signal of interest. For this
purpose, the adaptive filtering is the best option as the ambient noise is nonstationary in nature.
The next chapter is the adaptive filtering in which the basic of adaptive filtering and LMS
algorithm is discussed.

30
4. ADAPTIVE FILTERING
4.1 Introduction:
The usual method of estimating a signal (ࡿ෡) corrupted by additive noise is to pass it
through a filter that tends to suppress the noise (n) while leaving the signal (S) relatively
unchanged i.e. direct filtering.
Fig. 4.1 Filter
Filters used for direct filtering can be either Fixed or Adaptive.
Fixed filters:
The design of fixed filters requires a priori knowledge of both the signal and noise. In
other words, if the signal and noise properties are known, it is possible to design a fixed filter
that passes desired frequencies in the signal and rejects the unwanted frequencies.
Adaptive filter:
Adaptive filters adjust their impulse response to filter out the correlated signal in the
input, with or without a priori knowledge of the signal and noise characteristics. There is no need
of a priori knowledge, when the signal is narrowband, and noise is broadband, or vice versa. The
adaptive filters have the capability of adaptively tracking the signal under nonstationary
conditions.
The filter is used to reject unwanted signals and to achieve desired spectral characteristics
of a signal. The filter with fixed coefficient is useful to achieve the desired signal from the
additive noise, only when the signal and noise are stationary. In the condition when both the
signal and noise are nonstationary the fixed coefficient filter is unable to track the changes of
signal and noise. In such a condition, the adaptive filter is to be designed instead of the filter with
Filter ࡿ෡ࡿ + ࢔

31
fixed coefficient. When designing a Wiener filter a priori knowledge about the actual statistical
properties of the data to be processed is required. Only when the properties match the a priori
information required to the design of the filter is based, the filter is optimum. It may impossible
to design the Wiener filter because this information is not known completely and an appropriate
design may no longer be optimum. A possible solution is to first estimate the statistical
parameters of the appropriate signals and then compute the filter parameters.
The ambient noise is highly nonstationary signal which varies with the varying
environment in shallow water. The acoustic signal is the medium used for underwater
transmission. This acoustic signal gets highly affected by the unwanted ambient noise discussed
in chapter 2. It is also important to reconstruct the desired signal affected by the ambient noise. A
more efficient method is to use an adaptive filter. Hence the adaptive algorithm for filtering the
specific ambient noise from desired signal is required to be investigated. This work is includes
the implementation of adaptive filters to track the changes of signal and noise.
4.2 Adaptive Filtering:
Adaptive filtering involves the changing of filter coefficients over time, to adapt the
changing signal characteristics. Such a device is self-designing, in that the adaptive filter
operation is based on a recursive algorithm when the relevant information of the signal is not
available. The algorithm starts with a set of initial conditions, representing all information
available about the environment. In a stationary environment, after successive iterations of the
algorithm it converges, in average, to the optimum Wiener solution in some statistical sense. In a
non-stationary environment, the algorithm offers tracking capability, whereby it can track the
variations in the statistics of the relevant signals yielding some local solution, provided that the
variations are sufficiently slow. Adaptive digital filters are extremely useful devices in many
applications of digital signal processing [24].
4.2.1 Basic Elements of Adaptive filter:
There are developed many computationally efficient algorithms for adaptive filtering.
They are based on a statistical approach, such as LMS algorithm, or a deterministic approach,
such as RLS algorithm. Adaptive noise cancellation techniques are employed to mitigate the

unwanted noise effects. The most generally used structure in implementing adaptive filter is
transversal structure shown in Fig. 4.1.
a. Filtering Structure:
This module forms the output of the filter using measurement of
signals. The filtering structure is linear if the output is obtained as a combination of the input
measurement; otherwise it is said to non
parameters are adjusted by the adaptive fil
b. Criterion of the Performance (COP):
The output of the adaptive filter and desired response are produced by the COP module,
to assess its quality with respect to the requirements of the particular application
c. Adaptive Algorithm:
The adaptive algorithm uses the value of criterion of performance, or some function of it,
and measurements of the input and desired
the parameters of the filter to improve its performance.
Fig. 4.1 Basic Elements of Adaptiv
4.2.2 Mathematical model of Adaptive
The simplified structure of adaptive filter is shown in Fig. 4.2.
filter can be split into two main parts as filter part and update part. The function of the filter part
is to calculate the filter output
ral Analysis and Filtering of Ambient Noise in Shallow Water
32
transversal structure shown in Fig. 4.1.
This module forms the output of the filter using measurement of
measurement; otherwise it is said to non-linear. The structure is fixed by designer, and the
parameters are adjusted by the adaptive filters.
Criterion of the Performance (COP):
to assess its quality with respect to the requirements of the particular application
m uses the value of criterion of performance, or some function of it,
and measurements of the input and desired response (when available) to decide how to modify
the parameters of the filter to improve its performance.
Fig. 4.1 Basic Elements of Adaptive filter
Mathematical model of Adaptive Filter:
The simplified structure of adaptive filter is shown in Fig. 4.2. The transversal adaptive
te the filter output y(n), whereas the function of the update part is to adjust the set of
This module forms the output of the filter using measurement of the input signal or
linear. The structure is fixed by designer, and the
to assess its quality with respect to the requirements of the particular application.
m uses the value of criterion of performance, or some function of it,
when available) to decide how to modify
The transversal adaptive
whereas the function of the update part is to adjust the set of

N filter coefficients (wi), i=0,1,.....N
possible to a desired signal d(n).
Fig. 4.2 Simplified Structu
An adaptive filter is often referred as linear in the
obtained as a linear combination of
The mathematical model of adaptive filter
• Output signal: The output signal of the filter is given by,
where, wp(n) = weight vector of filter,
x(n-p)= samples of input signal
• Input noisy signal: The input signal
of a desired signal d(n) and interfering noise
• Error signal: The error signal e(n) is the difference between the desired signal
estimated signal (n)dˆ .
• Filter coefficients: The variable filter
structures the impulse response is equal to the filter coefficients. The coefficients for a filter
of order p is defined as
ral Analysis and Filtering of Ambient Noise in Shallow Water
33
), i=0,1,.....N-1(tap weights) so that the output y(n)
d(n).
Fig. 4.2 Simplified Structure of Adaptive filter
adaptive filter is often referred as linear in the sense that the output of the filter is
obtained as a linear combination of the available set of observations applied
The mathematical model of adaptive filter is discussed as follows,
The output signal of the filter is given by,
= weight vector of filter,
samples of input signal.
Input noisy signal: The input signal x(n) to the adaptive filter at the receiver side i
and interfering noise e(n) in the channel.
Error signal: The error signal e(n) is the difference between the desired signal
The variable filter has a Finite Impulse Response (FIR) structure. For FIR
)()(x(n) nend +=
T
nnn pwwww )](),....,1(),0([(n) =
)(ˆ)(e(n) ndnd −=
∑=
−⋅=
N
p
p pnxnw
0
)()(y(n)
y(n) reaches as close as
ense that the output of the filter is
observations applied to the filter input.
to the adaptive filter at the receiver side is the sum
Error signal: The error signal e(n) is the difference between the desired signal d(n) and the
has a Finite Impulse Response (FIR) structure. For FIR

34
• Impulse response: The variable filter estimates the desired signal by convolving the input
signal with the impulse response given by
• Update filter coefficients: Moreover, the variable filter updates the filter coefficients at every
time instant.
where, nw∆ is a correction factor for the filter coefficient. The adaptive algorithm generates
this correction factor based on the input and error signals.
There are three factors that measure the efficiency of an adaptive algorithm.
• The complexity of calculus measurements and the amount of calculus executed at each step
• The speed of adjustment that allows an adaptive filter to converge to the Weiner solution
• The estimation error obtained from the difference between the present Weiner solution and
the solution given by the adaptive algorithm.
The main function of adaptive filtering is the development of a filter which is able to
adjust the statistics of the signal. Normally, an adaptive filter applies the structure of a FIR filter,
with an adaptive algorithm that adjusts the values of its coefficients. The one of the importance
of adaptive filtering is signal estimation, in which the adaptive filter is to make all estimate of the
unknown signal. This class of application includes task of echo cancellation, noise cancellation,
adaptive array etc. Other applications of adaptive filtering are signal correction and signal
prediction. The LMS adaptive algorithm is used to filter the ambient noise in shallow water, to
reconstruct the desired signal.
4.3 Least Mean Square Algorithm (LMS):
The LMS algorithm is a linear adaptive filtering algorithm, consists of two basic
processes: a filtering process and an adaptive process. The LMS algorithm is built on the
)(*(n)dˆ nxwn=
nnn www ∆+=+1

35
transversal filter concept. This component is efficient for performing the filtering process using a
mechanism for achieving the adaptive control process on the tap weights of the transversal filter.
The LMS algorithm can be written in the form of three basic relations as,
1. Adaptive filter output:
)()(ˆ)( nxnwny T
=
2. Estimation error or error signal is
e(n) = d(n) - y(n)
3. Tap-weight adaptation is given by
ŵ(n + 1) = ŵ(n) + µx(n)e(n)
where e(n)= the error signal,
x(n) = the input signal vector,
µ = the small step-size parameter,
ŵ(n) = the tap-weight vector,
d(n) = the desired response.
At each iteration or time update, this algorithm requires understanding of the most recent
values: u(n), d(n) and ŵ(n). Here, µ is the step-size that controls the convergence speed and
stability of the LMS adaptive filter. Unfortunately as the step size parameter reduced the rate of
convergence of LMS algorithm correspondingly get decreased. For the stability of the algorithm,
the step size parameter should satisfy the following relation for large length of the filter.
0 < ߤ <
2
‫ܵܯ‬௠௔௫
where M is the length of filter and ܵ௠௔௫ is the maximum value of PSD of the tap input u(n).
For the study of statistical performance of the adaptive filter, the Mean-square Error
learning curve and Mean-Square Deviation (MSD) learning curves can use. The MSE learning

36
curve is based on the ensemble averaging of squared estimated error (i.e. curve of MSE versus
number of iterations).
In this chapter, the detailed study of the concept adaptive filtering theory as well as a
mathematical model is discussed. The standard algorithm of LMS adaptive filter is presented.
The LMS algorithm has simplicity in implementation and hence useful in the application of
adaptive noise cancellation. In this work, the ambient noise is filtered from the desired signal
using LMS algorithm. The results and performance of the LMS algorithm is discussed in the next
chapter.

37
5. RESULTS AND DISCUSSION
5.1 Database:
The ambient noise data have been recorded using four fixed underwater sensors placed at
30 m depth from the sea surface. The sea bottom is at in the area around the sensor with soft mud
being close to the mouth of a river. The four sensors have been placed horizontally along the east
west direction with 100 m spacing between the sensors. Data recording is undertaken by all the
four sensors simultaneously, and signal processing is carried out on the recorded data
independently. The recorded data is digitized at the sensor itself at 256 KHz rate post filtering by
an anti-aliasing filter. The digitized data is received by an underwater junction box where it is
put in serial format and modulated to optical format and transmitted to the shore based data
handling system via a fiber optic cable for further processing. The specification of the sensors is
given in Table-5.1.
Table- 5.1 Specifications of Hydrophone
Specification Description
Hydrophone Type ITC 8264
Sensitivity -175 dB re 1 _Pa
Bandwidth 10 Hz - 100 KHz
Beam pattern
Horizontal Omni-Directional, +/-2 dB Vertical
Omni-Directional, +/-2 dB for upper hemisphere
Gain
6 dB to 6 dB + 90 dB in remotely controlled steps
of 6 dB
Anti-Alias Pass Band Dc to 100 KHz
Sample rate 262144 Ks/sec, +/- 2 Hz
Resolution 16 bits
Linearity ADC only
S/N + distortion ADC only
The ambient noise data was recorded periodically eight times over the day at an interval
of one hour for the duration of five minutes. The timing of the data recording was such that it

38
captured the diurnal environmental variation of a tropical region, starting with low temperatures
in the morning followed by higher temperatures and wind speed with the sun going up and then
the temperature falling towards the evening. It captures the broad spectrum of the diurnal
variation in the surface parameters of the sea. The location of the sensors is such that the impact
of distant shipping is more prominent among the ambient noise sources.
5.2 Spectral Analysis of Ambient Noise:
The nonparametric methods such as periodogram and periodogram based spectral
estimation techniques are used to analyze the ambient noise in shallow water.
5.2.1 Spectra of ambient noise in shallow water: ( PSD of AN)
Here an ambient noise in shallow water is analyzed using the modified periodogram
methods like, Bartlett and Welch. The spectrum shows the variation of ambient noise level as a
function of frequency. The power spectrum of the ambient noise recorded on 8th
Jan and 25th
March is shown in figure below. Here the Bartlett and Welch method of estimation of PSD is
used with the specifications given in Table 5.2.
Table- 5.2 Specifications of PSD Algorithms
Parameter Value
Sampling Frequency 44100Hz
Window Type Hanning
N-point-FFT 65536
FFT window size 1024
Overlapping (for Welch method) 50%

39
Fig. 5.1 PSD of signal 13-1B68EI
Fig. 5.2 PSD of signal 24-3B103EI
10
0
10
1
10
2
10
3
10
4
-20
-10
0
10
20
30
40
50
frequency(Hz)
Power(dBre1micropa
2
/Hz)
PSD of 21-1 B68
Bartlett
Welch
10
0
10
1
10
2
10
3
10
4
-20
-10
0
10
20
30
40
50
60
frequency(Hz)
Power(dBre1micropa
2
/Hz)
PSD of 24-3 B103
Bartlett
Welch

40
As the ocean environment is highly variable, there can be possible different sources
causes the variation in ambient noise level. Each ambient noise source is having its own
signature frequency so that from spectrum of ambient noise, we can analyze that which source is
dominant. From the spectrum the ambient noise is dominant in the frequency range between 10
to 1000 Hz i.e. the low frequency band.
Therefore it can be say that the variation in ambient noise is due the distant shipping or
marine mammals, because these are the dominant sources in low frequency range. Also there are
different factors which can cause the variation in ambient noise level such as wind speed,
temperature, tide height etc.
5.2.2 Analysis of AN using curve fitting tool
Best fit tool (pdf matching):
The ambient noise analysis process is a random one and time-series results must be
treated statistically. The simplest way is to describe the time series in terms of its underlying
energy probability density function (pdf), estimated from a suitably large set of data to provide a
smooth estimate. Estimating the pdf’s for the PSD of ambient noise obtained using the best pdf
fitting tool. Fit one of three probability distributions (normal, lognormal, weibull) to input data
vector. If the distribution is specified as 'best' the distribution that best fits the data is selected
automatically based on maximum likelihood estimates.

41
(a)
(b)
Fig. 5.3 pdf Fitting
-30 -20 -10 0 10 20 30 40
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Data
ProbabilityDensity
pdf B68
HIstogram of NSL
Normal (Best)
Lognormal
Weibull
-60 -40 -20 0 20 40
0
0.02
0.04
0.06
0.08
0.1
0.12
Data
ProbabilityDensity
pdf B103
Histogram of NSL
Normal
Lognormal (Best)
Weibull

42
The histogram of the ambient noise levels is shown by the bars and the different pdf’s are
shown by solid lines of different colors. The best pdf fit tool is applied to the noise spectral level
of all the recorded data of ambient noise. It is observed that the most of the time series of data
fits with the normal distribution. The distribution fit is also applied for the spectrum of ambient
noise. The spectrum of data is fit in all of three pdf’s. It is observed that on an average the
lognormal is the best fit pdf for the spectrum of ambient noise data.
5.2.3 Diurnal variation of ambient noise:
The ocean environment is variable with respect to time or season. The ocean traffic keeps
changing day by day and time by time. Data has been collected at different times in a day from
9:30 am to 4:30 pm on an hourly basis, in the month of January and March. As there are different
factors like temperature, wind speed etc. can affect the ambient noise spectrum. These factors are
more variable throughout a day. In general temperature in is low at 9:30 pm, start rising and
reaches its maximum level during mid-day and then it goes down. Also wind is depends on
temperature variation. Therefore these two factors can be responsible for increase or decrease in
noise level.
Fig. 5.4 Diurnal Analysis of Ambient Noise

43
Above figure shows the time wise average of ambient noise level for January data of
ambient noise. The averages are calculated for each day of January on which recording were
taken at particular time. From the graph we can say that the variation in ambient noise is
significantly observed. It may due to fluctuations in temperature occur at different time
throughout a day. The maximum ambient noise is observed at time 1:30pm and 2:30pm. At this
time of day the temperature is reaches at maximum. Therefore this variation in ambient noise
level may due to molecular agitation because of increasing temperature. This effect is dominant
in the region of frequency spectrum few Hz to 1 KHz.
5.2.4 Seasonal variation of AN: (January and March )
To analyze the ambient noise on the basis of seasonal effect, here the average of the
power spectrum calculated for all the days in January and March separately.
Fig. 5.5 Seasonal Variation of Ambient Noise
From the graph it is clearly observed that the average ambient noise level in March is
high as compared to average ambient noise in January throughout the frequency range. The

44
reason of this is that the average wind speed is higher in the March than in January. Also
temperature is high in the month of March as compared in January. Hence it can be considered
that the wind is dominating factor so that increase in the noise level in March.
5.2.5 The effect of wind speed on ambient noise level:
PSD of an ambient noise is estimated for various wind speeds (1.03m/s, 2.06m/s,
2.58m/s, 3.08m/s and 4.64m/s). Since wind exists in all places and at all time, its effect plays a
major role in variation of ambient noise in a shallow water environment. For the estimated PSD
spectra this variation is observed over the entire range of frequency. In low frequency range the
ambient noise increases as the wind speed increases. Wind noise dominates the noise of distant
shipping over the entire frequency. If the noise level is is related to wind speed, in the range of
KHz’s frequencies.
Fig. 5.6 Effect of Wind Speed
5.2.6 Regression analysis of ambient noise level w.r.t. wind speed
The spectral analysis was carried out using Welch method of averaging periodogram.
First the noise level in dB was plotted against frequency for different wind speed. The frequency
10
-1
10
0
10
1
10
2
10
3
10
4
10
5
-40
-20
0
20
40
60
80
100
frequency(Hz)
PSD(dBre1micropa2
/Hz)
PSD using Welch for varying Wind speeds
1.03m/s
1.56m/s
2.06m/s
2.58m/s
3.08m/s
4.64m/s

45
range of interest for the current study was from 500 Hz to 7 kHz where the best correlation
between the wind speed and the noise level has been observed. The estimated PSD by using
Welch method is then analyzed by regression plot as shown in above figure. The ambient noise
level for different wind speeds at different frequencies is observed.
Fig. 5.7 Regression analysis of Wind dependant Ambient Noise
Table 5.3: Analysis of Regression Plot
Freq
(kHz)
Y-intersect
B
Slope
n
1 17.24 2.61
2 11.60 3.42
3 11.29 -0.20
4 11.06 1.10
5 9.54 -0.23
6 5.99 0.34
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
5
10
15
20
25
30
wind speed(m/s)
NSL(dBre1Micropa2/Hz)
WSE Regression Plot
1KHz
2KHz
3KHz
4KHz
5KHz
6KHz
7KHz
8KHz

46
7 1.37 0.17
8 3.62 -0.05
Figure shows that the ambient noise level increases with increasing wind speed at low
frequency. There are more variations in ambient noise level with increasing wind speed at higher
frequency. This analysis is listed in Table 1.3. B is the Y-intersect and n is the slope of regression
line. Slope of regression line decreased as increase in frequency with increasing wind speed.
5.2.7 Effect of shipping on AN level:
Effect of ambient noise is observed due to low and heavy shipping. The average power
spectrum is calculated for the ambient noise data recorded in low and heavy traffic of distant
shipping in the month of January as well as in March. The average ambient noise due to heavy
shipping is observed significantly high as compared to that of due to low shipping.
Fig. 5.8 (a) Effect of Shipping on Ambient Noise
10
0
10
1
10
2
10
3
10
4
-20
-10
0
10
20
30
40
50
60
70
80
Frequency (Hz)
Power(dBre1MicroPascal2
/Hz)
Heavy Shipping
Medium Shipping
Low Shipping
Heavy Shipping
Low Shipping

47
Also these curves are compared with the standard curves of ambient noise related with
shipping prepared by Wenz (1962). It is noticed that the curves for low shipping is exactly
matched with the corresponding curves for estimated spectra. But the computed spectrum of
heavy shipping data is matching with the standard curve of moderate shipping. The difference
between the standard heavy shipping curve and computed spectrum of heavy shipping curve is
near about 8dB. The average PSD is computed for the ambient noise under low and heavy
shipping. Here also the effect of heavy shipping is dominant than the effect of low shipping.
From the graph the noise level is dominates in the range of low frequency spectrum i.e.
from 10 Hz to 500 Hz and in this frequency range the ambient noise is due to low shipping and
moderate shipping. So that it can be conclude that the variation in ambient noise level is due to
the distant shipping is dominant in Goa coast.
5.3 Adaptive algorithms of Ambient Noise:
The ambient noise in ocean environment affects the underwater acoustic signal
transmission. This effect can be reduced by adaptive cancelling the ambient noise from the signal
of interest i.e. desired signal. The main in designing an adaptive filter is to reach certain accuracy
soon as possible with the least amount of complexity. Here the LMS adaptive algorithm is used
for noise cancellation of ambient noise.
The experiment is performed for the generated simulated signal of the frequency same as
that of the ambient noise. The algorithm is examined using the performance parameters like SNR
and MSE of output signal. The results are achieved for the different step size. The LMS
algorithm is simpler in evaluation process. To improve the SNR and to reduce the MSE the
different adaptive algorithm is to be investigated.

48
Fig.
The Fig. 5.10 shows the filtering of noise using LMS adaptive algorithm. The ambient
noise generated by dolphin which is having signature frequency about 775 Hz. This noise is
added with the sine signal of same frequency and given as a filter input. The Fig. 5.10(a) shows
the noisy input to the LMS filter of order 100 and achieved initial filter response shown in Fig.
5.10(b). The weight adaptation process is carried out using LMS adaptive algorithm with small
step size (µ=0.015). The final output of the LMS algorithm shows that noise is removed from the
desired signal. The error signal is difference between the desired signal and the estimated signal.
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(a)
Amplitude
Desired Signal(500 Hz sine Signal)
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(b)
Amplitude
Ambient Noise
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(c)
Amplitude
500 Hz sine + Ambient Noise
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(d)
Amplitude
Output of LMS algorithm
Desired
Reconstructed
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(e)
Amplitude
Error Signal

49
0 500 1000 1500 2000
1
2
3
4
Spectrogram of 500 Hz Signal
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Ambient Noise
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
500 Hz + Ambient Noise
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Recovered signal
Frequency (Hz)
Time

50
Fig. 5.10 Filtering of Dolphin Sound
(a)Desired Signal (b) Dolphin (c) 775Hz sine +Dolphin (d) output of LMS algorithm
(e) Error Signal
The SNR and MSE obtained using LMS adaptive filtering is 32.4070dB and 0.0002866.
To improve the SNR and to reduce the MSE the different adaptive algorithm such as NLMS
(Normalized LMS), RLS is to be investigated.
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(a)
Amplitude
Desired Signal(775 Hz sine Signal)
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(b)
Amplitude
Dolphin
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(c)
Amplitude
775Hz sine + Dolphin
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(d)
Amplitude
Desired
Reconstructed
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(e)
Amplitude
Error Signal

51
Fig. 5.11 Evaluation of LMS algorithm (Filtering of Dolphin )
The performance of the LMS algorithm is evaluated by using the time-frequency analysis
STFT technique. Spectral as well as the temporal characteristics of the signal is analyzed using
Spectrogram. The Fig. 5.11 shows the evaluation of performance of the LMS adaptive algorithm.
Fig. 5.11(a) shows the spectrogram of the desired signal which shows the signal of 775
Hz frequency is exists for all time. The Fig. 5.11(b) shows the spectrogram of the ambient noise
by dolphin which is having the maximum strength at the frequency about 775 Hz. This noise is
added with desired signal i.e. the noisy input signal to the LMS adaptive filter. The spectrogram
of this noisy signal as in Fig. 5.11(c) shows the spectral as well as the temporal characteristics of
noise and desired signal. The Fig. 5.11(d) shows spectrogram of the output signal of the LMS
filter. It is observed that the spectrogram shows the maximum strength at the frequency of 775Hz
for all time which matches the spectral characteristics of desired signal.
0 500 1000 1500 2000
1
2
3
4
Spectrogram of 775 Hz Signal
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Dolphin
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
775 Hz + Dolphin
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Recovered Signal
Frequency (Hz)
Time

52
0 2 4 6 8 10
x 10
4
-2
0
2
Samples
(a)
Amplitude
Desired Signal(1 KHz sine Signal)
0 2 4 6 8 10
x 10
4
-2
0
2
Samples
(b)
Amplitude
Ship
0 2 4 6 8 10
x 10
4
-2
0
2
Samples
(c)
Amplitude
1 KHz sine + Ship
0 2 4 6 8 10
x 10
4
-2
0
2
Samples
(d)
Amplitude
Desired
Reconstructed
0 2 4 6 8 10
x 10
4
-2
0
2
Samples
(e)
Amplitude
Error Signal

53
0 500 1000 1500 2000
1
2
3
4
Spectrogram of 1 KHz signal
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Ship
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
1 KHz + Ship
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Frequency (Hz)
Time

54
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(a)
Amplitude
Desired Signal(1.3 KHz sine Signal)
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(b)
Amplitude
Humpback Whale
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(c)
Amplitude
1.3 KHz sine + Humpback Whale
0 0.5 1 1.5 2
x 10
5
-2
0
2
Samples
(d)
Amplitude
Output of LMS Filter
Desired
Reconstructed
0 0.5 1 1.5 2
x 10
5
-2
-1
0
1
2
Samples
(e)
Amplitude
Error Signal

55
Sr. No. Input to LMS Filter SNR MSE
1. 500 Hz + Ambient Noise 40.9380 dB 4.0269× 10-5
2. 775 Hz + Dolphin 34.4870 dB 1.7807× 10-4
3. 1 KHz + Ship 35.2814 dB 1.4805 × 10-4
4. 1.3 KHz + Humpback Whale 30.4524 dB 4.5033 × 10-4
0 500 1000 1500 2000
1
2
3
4
Spectrogram of 1.3 KHz signal
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Spectrogram of Humpback Whale
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
1.3 KHz + Humpback Whale
Frequency (Hz)
Time
0 500 1000 1500 2000
1
2
3
4
Frequency (Hz)
Time

56
6. CONCLUSION AND FUTURE SCOPE
The ambient noise in is analyzed in frequency domain using nonparametric methods of
estimation of PSD. It is reported that the modified periodogram (Bartlett and Welch) methods are
well suited for the analysis of recorded data in the presence of noise. For the analysis purpose,
the different natural parameters such as wind speed, temperature, tide height etc. and man-made
activities such as shipping etc. at the time of recording data are taken in to consideration. From
the results of spectral analysis of ambient noise it is observed that the noise level varies with
change in parameters, like wind speed, temperature, tide height.
The effect of varying wind speed on ambient noise level is observed and verified by
using regression technique. Wind exists all time, so the effect of wind speed contributes in the
spectral noise level throughout the frequency range. From this analysis it can conclude that the
noise level increases with increase in wind speed and variations decreases as frequency
increases. It is also reported that the ambient noise variation is depends on temperature. For this
analysis the PSD of the signals recorded at different time is observed. It can say that the ambient
noise level is high at the time of 1:30pm and 2:30pm as that of in the morning and evening time.
Therefore, it can be say that the ambient noise level increases with increase in the temperature.
This is again observed in the results of seasonal analysis of the ambient noise. The average PSD
of the data in the month of January and March is computed. The result shows that the average
ambient noise level is high in the month of March as compared to that of in the January. This
may due to the effect of wind speed and temperature on ambient noise level. Generally the wind
speed and temperature in March is high than that of in the January.
The statistical analysis is performed for the time series and spectrum of database. The
best pdf fit tool is applied to the noise spectral level of all the recorded data of ambient noise. It
is observed that the most of the time series of data fits with the normal distribution. The spectrum
of data is fit in all of three pdf’s. It is observed that, on an average the lognormal is the best fit
pdf for the spectrum of ambient noise data.
The man-made activities such as shipping also contribute in the underwater ambient
noise. From the results of PSD it is reported that the shipping noise dominate low frequency

57
range below 1 kHz. Noise spectral level due to low traffic and heavy traffic is analyzed. It is
observed that the noise level due to heavy shipping is high than low shipping ambient noise.
Removal of ambient noise adaptively from the signal of interest is another important
issue. Here, the LMS adaptive algorithm is used for noise cancellation of different ambient noise
such as Dolphin, ship, Humpback Whale noise etc. The experiment is performed for the different
step size. The LMS algorithm is able to filter the ambient noise at acceptable level. The results of
LMS algorithm is evaluated by performance parameters SNR and MSE. To compare the
performance of LMS algorithm, it is required to investigate the different adaptive algorithms like
NLMS, RLS etc. And from the comparison of the SNR and MSE achieved by different
algorithms, it can be able to make conclusion about the best adaptive algorithm for filtering
adaptive noise from the desired signal.

58
References
[1] Peter H. Dahl, James H. Miller, Douglas H. Cato, Rex K. Andrew, “Underwater Ambient Noise”,
Acoustics Today, January 2007.
[2] John A. Hildebrand, “Anthropogenic and natural sources of ambient noise in the ocean”, Acoustics
in marine ecology Vol. 395: 5–20, 2009.
[3] Gordon M. Wenz, “Acoustic Ambient Noise in the Ocean: Spectra and Sources”, The Journal of the
Acoustical Society of America Vol 34. No. 12 December 1962.
[4] R. J. Urick, “Ambient Noise in the Sea”, Undersea Warfare Technology Office Naval Sea Systems
Command Department of the Navy Washington, D.C. 20362, 1984
[5] Donald Ross, “Ship Sources of Ambient Noise”, IEEE Journal of Oceanic Engineering, Vol. 30, No.
2, April 2005.
[6] Discovery of the Sound in the Sea website. [Online]. Available: http://www.dosits.org
[7] Christine Erbe, “Underwater Acoustics: Noise and the Effects on Marine Mammals”, A Pocket
Handbook 3rd Edition.

59
[8] S. Sakthivel Murugan, V. Natarajan, R. Rajesh Kumar, “Estimation Of Noise Model and Denoising
of Wind Driven Ambient Noise in Shallow Water Using The LMS Algorithm” Acoustics Australia
Vol. 40, No. 2, August 2012 – 111.
[9] V. Vijayabaskar, V. Rajendran, “Wind Dependence of Ambient Noise in Shallow Water of Arabian
Sea during Pre-Monsoon”, IEEE, 978-1-4244-9182-7/10, 2010.
[10] Paul C. Etter, “Underwater Acoustic Modeling and Simulation”, SPON Press.
[11] Tan Soo Pieng, Koay Teong Beng, P. Venugopalan, Mandar A. Chitre and John R. Potter,
“Development of a Shallow Water Ambient Noise Database”
[12] John R. Potter, Lim Tze Wei & Mandar Chitre, “Ambient noise environments in shallow tropical
seas and the implications for acoustic sensing”, Acoustic Research Laboratory, c/o Dept. Physics,
NUS, 10 Kent Ridge Crescent, Singapore 119260.
[13] John R. Potter , Lim Tze Wei & Mandar Chitre , “High-frequency ambient noise in warm shallow
waters”
[14] Matthew W. Legg, Alec J. Duncan, “Analysis of impulsive biological noise due to snapping shrimp
as a point process in time”, IEEE, 2007
[15] Dimitris G. Manolakis, Vinay Ingle, Stephen M. Kogon, ‘Statistical and Adaptive Signal
Processing’, McGraw- HILL International Edition.
[16] M. S. Bartlett, “Periodogram Analysis And Continuous Spectra”, Volume 37, Parts 1 And 2 June
1950
[17] Soosan Beheshti, Maryam Ravan, James P. Reilly and Laurel J. Trainor, “Mean-Squared Error in
Periodogram Approaches with Adaptive Windowing” IEEE Transactions on Signal Processing, Vol.
59, No. 3, March 2011 923.
[18] A. Das, “Shallow Ambient Noise Variability Due to Distant Shipping Noise and Tide,” Applied
Acoustics, Vol.72, pp. 660–664, 2011.
[19] M. Abbey, J. McNames, Oscar W. Marquez, R. Hornero, T. Thong, B. Gold Stein, “Power Spectral
Estimation and Tracking of Non-Stationary Pressure signals based on Kalman Filtering”, 26th
Annual International Conference of the IEEE EMBS, 2004.
[20] Tegowski J., Deane G., Lisimenka A. and Blondel P., “Spectral and Statistical Analysis of Ambient
Noise in Spitsbergen Fjords and Identification of Glacier Calving Events”, 11th
European
Conference on Underwater Acoustics 2012, ECUA 2012. Institute of Acoustics, St. Albans, pp.
1667-1672.
[21] R. Rajesh Kumar, S. Sakthivel Murugun, V. Natarajan, S. Radha, “Analysis of Power Spectral
Density and Development of an Adaptive Algorithm for Filtering Wind Driven Ambient Noise in

60
Shallow Water”, IEEE, 2011.
[22] S. Kiruba Veni, S. Sakthivel Murugun, S. Radha, “Adaptive Algorithms For Detection Of
Underwater Acoustic Signals Against Ambient Noise In Shallow Sea”, IEEE 2011
[23] Komal R. Borisagar , Dr. G. R. Kulkarni , “Simulation and Comparative Analysis of LMS and RLS
Algorithms Using Real Time Speech Input Signal” Global Journal of Researches in Engineering 44
Vol.10 Issue 5 (Ver1.0)October 2010
[24] Simon Haykin, Thomas Kailath, ‘Adaptive Filter Theory’, Pearson Education, 4th
edition.

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