The document discusses colored noise removal and channel equalization from a noisy audio signal. It describes different types of colored noises like pink noise and blue noise which are generated by passing white noise through a shaping filter. It also discusses how the audio signal can be corrupted by noise and the channel transfer function. It proposes using a filter to remove noise and an equalizer with a transfer function inverse to the channel function for channel equalization. Adaptive linear equalizers using algorithms like LMS are also summarized for updating the equalizer weights.
Acoustic problems in an environment has gained more attention due to the tremendous growth of technology that lead to noisy engines, heavy machineries, pumps, air condition, music and other noise sources. Normally human ears are very sensitive at audio range (lower frequency) from 20 Hz to 20 kHz. So, any sound within these frequencies has the tendency to disturb human hearing and can be classified as noise. The reduction of acoustic noise in speech has been investigated for many Years .The major application of noise reduction is by improving voice communication and eliminating the noise using adaptive noise canceler.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
Acoustic problems in an environment has gained more attention due to the tremendous growth of technology that lead to noisy engines, heavy machineries, pumps, air condition, music and other noise sources. Normally human ears are very sensitive at audio range (lower frequency) from 20 Hz to 20 kHz. So, any sound within these frequencies has the tendency to disturb human hearing and can be classified as noise. The reduction of acoustic noise in speech has been investigated for many Years .The major application of noise reduction is by improving voice communication and eliminating the noise using adaptive noise canceler.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas
such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal
processing applications. The key to successful adaptive signal processing understands the fundamental
properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the
noise component which is overlap with undesired signal in the same frequency range. This paper presents
design, implementation and performance comparison of adaptive FIR filter using LMS and RMS
algorithms. MATLAB Simulink environment are used for simulations
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
the presentation consists of a brief description about ADAPTIVE LINEAR EQUALIZER , its classification and the associated attributes of ZERO FORCING EQUALIZER and MMSE EQUALIZER
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
In noisy acoustic environment, audio signal in speech communication from mobile phone, moving car, train, aero plane, or over a noisy telephone channel is corrupted by additive random noise. The noise is unwanted signal and it is desirable to remove noise from original signal. Since noise is random process and varying at every instant of time, we need to estimate noise at every instant to remove it from original signal. There are many schemes for noise removal but most effective scheme to accomplish noise cancellation is to use adaptive filters. In this paper, we have carried out simulations for different adaptive algorithms (LMS, NLMS and RLS) and compared their performance for noise cancellation in noisy environment. Real time implementation of adaptive algorithm over DSP kit (TMS320C6713) is also presented in this paper. Performance of adaptive algorithm over hardware is also presented. Developed system incorporating best performance adaptive filter in any noisy environment can be used for noise cancellation.
Equalization is a technique, which is introduced to remove interference after received the signal. This works on receiver side. This is like the extension of simple Transmission system...
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas
such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal
processing applications. The key to successful adaptive signal processing understands the fundamental
properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the
noise component which is overlap with undesired signal in the same frequency range. This paper presents
design, implementation and performance comparison of adaptive FIR filter using LMS and RMS
algorithms. MATLAB Simulink environment are used for simulations
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
the presentation consists of a brief description about ADAPTIVE LINEAR EQUALIZER , its classification and the associated attributes of ZERO FORCING EQUALIZER and MMSE EQUALIZER
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
In noisy acoustic environment, audio signal in speech communication from mobile phone, moving car, train, aero plane, or over a noisy telephone channel is corrupted by additive random noise. The noise is unwanted signal and it is desirable to remove noise from original signal. Since noise is random process and varying at every instant of time, we need to estimate noise at every instant to remove it from original signal. There are many schemes for noise removal but most effective scheme to accomplish noise cancellation is to use adaptive filters. In this paper, we have carried out simulations for different adaptive algorithms (LMS, NLMS and RLS) and compared their performance for noise cancellation in noisy environment. Real time implementation of adaptive algorithm over DSP kit (TMS320C6713) is also presented in this paper. Performance of adaptive algorithm over hardware is also presented. Developed system incorporating best performance adaptive filter in any noisy environment can be used for noise cancellation.
Equalization is a technique, which is introduced to remove interference after received the signal. This works on receiver side. This is like the extension of simple Transmission system...
introduction to pulse shaping and equalization in advanced digital communication, it's characterisation, signal design of band limited signal, design of bandlimited signal for no ISI and design of bandlimited signal with controlled ISI-partial response, linear equalization,
the modulation of a wave by varying its amplitude, used especially as a means of broadcasting an audio signal by combining it with a radio carrier wave.
1. COLOURED NOISE REMOVAL AND
EQUALISING THE CHANNEL EFFECT
FROM A NOISY AUDIO SIGNAL
G. Anudeep Reddy (EC08484)
1 G. Madhuri (EC08485)
2. INTRODUCTION
We want to transmit a song signal.
Song Signal Transmitter
Loud Speaker Receiver
2
3. REMOVAL OF NOISE
The signal may be corrupted by Noise.
Song
Transmitter Noise
Signal
Receiver
3
4. REMOVAL OF NOISE
The signal may be corrupted by Noise.
Song
Transmitter Noise
Signal
Loud
Filter Receiver
Speaker
4
5. WHITE NOISE
White noise is a signal (or process), having
equal power in any band of a given bandwidth
(power spectral density).
5
6. COLORED NOISE
Based on Spectral density (power distribution
in the frequency spectrum) we can distinguish
different types of noise.
This classification by spectral density is given
"color" terminology, with different types
named after different colors.
6
7. COLORED NOISE
The color names for these different types of
sounds are derived from a loose analogy
between the spectrum of frequencies of sound
wave present in the sound and the equivalent
spectrum of light wave frequencies.
That is, if the sound wave pattern of "blue
noise" were translated into light waves, the
resulting light would be blue, and so on.
7
8. PINK NOISE
Similar to White Noise except the power
density decreases 3 dB per octave as the frequency
increases. In technical terms the
density is inversely proportional to the frequency.
8
9. BLUE NOISE
Similar to White Noise except the power
density increases 3 dB per octave as the frequency
increases. In technical terms the
density is proportional to the frequency.
9
10. GENERATION OF COLORED NOISE
Colored noise can be generated by passing the
white noise through a shaping filter.
The response of the colored noise can be
varied by adjusting the parameters of the
shaping filter.
White Shaping Colored
Noise Filter Noise
10
11. REMOVAL OF CHANNEL EFFECT
The signal may be corrupted by channel
transfer function also.
Song
Transmitter Channel Noise
Signal
Receiver
11
12. REMOVAL OF CHANNEL EFFECT
The signal may be corrupted by channel
transfer function also.
Song
Transmitter Channel Noise
Signal
Loud
Filter Equalizer Receiver
Speaker
12
13. EQUALIZER
The equalizer should have transfer function
which is inverse of channel.
1
H (z)
C (z)
Where H(z) is the transfer function of equalizer
and C(z) is the transfer function of channel.
But in most of the cases we do not know the
transfer function of the channel, so we will
adapt the equalizer transfer function using
Learning algorithm.
13
15. CHANNEL AND CHANNEL EQUALIZER
o A finite impulse response (FIR) filter is a type of a
signal processing filter whose impulse response (or
response to any finite length input) is of finite duration,
because it settles to zero in finite time.
o This is in contrast to infinite impulse response (IIR)
filters, which have internal feedback and may continue
to respond indefinitely (usually decaying).
o The impulse response of an Nth-order discrete-time
FIR filter, lasts for N+1 samples, and then dies to zero.
15
16. o FIR filters can be discrete
time or continuous-time, and digital or analog.
16
17. For a discrete-time FIR filter, the output is a
weighted sum of the current and a finite number of
previous values of the input. The operation is
described by the following equation, which defines the
output sequence y[n] in terms of its input
sequence x[n]:
o The channel can be considered as a discrete
time digital FIR filter
17
18. o Similarly the equalizer can be considered as an FIR
filter(discrete time, digital FIR filter)
From the block diagram, it is evident that the optimal
equalizer should have transfer function which is inverse
of channel. Hence channel equalization is also known as
inverse filtering.
Transfer function of Channel , C(z)= b0+ b1z-1+ b2z-2 + bnz-n
Transfer function of the Equalizer, H(z)= 1/C(z)
18
19. AN ADAPTIVE LINEAR EQUALIZER
xk-1 xk-2 xk-L+1
xk Z-1 Z-1 Z-1
w0k w1k w2k w(L-1)k
∑
yk
There is an input signal vector, x 0 , x1 ... x L 1
a corresponding set of adjustable weights, w 0 , w1 ... w L 1
19
a summing unit, and a single out put signal.
20. ADAPTIVE LINEAR EQUALIZER
o A procedure for adjusting or adopting the weights is
called weight adjustment or adaptation procedure.
o The combiner is called linear because for fix setting of
weights its output is a linear combination of the input
components.
o The output of the combiner can be represented as
L
yk w lk x k l
l 0
where w lk denotes l th weight at k th instant.
20
21. If the weight and input vectors are expressed as
T
Xk [ x0 k x1 k x ( L 1) K
]
T
Wk [ w0 k w1 k w ( L 1) K
]
then the output is given by
T
yk xk wk
The weights of the combiner are to be updated
using various learning algorithms
Ravi Kumar Jatoth Department 21
of ECE NITW
22. LEARNING ALGORITHMS
LMS Algorithm
RLS Algorithm
Kalman Filter
Neural Algorithm
Fuzzy Logic System
Optimization Algorithms
All the algorithms update the weights of the
equalizer using different cost functions. 22
Ravi Kumar Jatoth Department
of ECE NITW
23. LEAST MEAN SQUARE ALGORITHM
❏ LMS: adaptive filtering algorithm having
two basic processes
✔ Filtering process, producing
1) output signal
2) estimation error
✔ Adaptive process, i.e., automatic
adjustment of filter tap weights
23
24. LEAST MEAN SQUARE ALGORITHM
o LMS algorithm is one of the conventional
techniques applied to channel equalization. The cost
function is Mean Square Error (MSE). It updates the
weights of the adaptive FIR filter based on the error
obtained. The instantaneous error at any time-step 'k'
can be represented as
e(k) = d(k) – y(k)
where d(k) delayed input reference is signal at time-
step „k‟, and 'y(k)‟ is estimated output from equalizer.
24
25. o The equalizer filter's impulse response vector is
adapted using the following equation,
w(k+1) = w(k) + 2µ.e(k).x(k)
where µ is called „Convergence factor’ or ‘Learning
rate parameter’, (0 ≤ µ ≤ 1).
x(k) Is input from transmitter at time-step 'k'.
o This procedure is repeated till the Mean Square Error
(MSE) of the network approaches a minimum value.
25
26. STABILITY OF LMS
More practical test for stability is
2
0
input signal power
Larger values for step size
Increases adaptation rate (faster adaptation)
Increases residual mean-squared error
26
351M Digital Signal Processing
27. xk-1 xk-2 xk-L+1
xk Z-1 Z-1 Z-1
w0k w1k w2k w(L-1)k
∑
ek yk
LMS -
Algorithm ∑
+
dk
Fig. 2 Adaptive filter using LMS algorithm
T
Xk xk xk 1
xk L 1 the L-by-1 tap input vector.
T
27
Wk w0 k w1 k w L 1 k the L-by-1 tap weight vector
29. NUMERICAL EXAMPLE- CHANNEL EQUALIZATION
❏ Transmitted signal: random sequence of
±1‟s.
❏ The transmitted signal is corrupted by a
channel.
❏ Channel impulse response:
29
30. ❏ The amplitude distortion, and eigen value spread,
were controlled by W.
The received signal is processed by a linear, 11-tap
FIR equalizer adapted with the LMS algorithm
30
32. REFERENCES
“Digital Signal Processing using MATLAB” demos
by Charulatha Devi.
Georgi Illiev and Nikola Kasabov, "Channel
Equalization using Adaptive Filtering with
Averaging", University of Otago, Newzeland.
M Reuter, J Zedlier, "Nonlinear effects in LMS
adaptive equalizers", IEEE Trans.Signal
Processing, June1999.
32