This document presents a paper on improving the intelligibility of Telugu speech patterns using a wavelet-based hybrid threshold transform method. It discusses how noise can reduce speech intelligibility in industrial areas and for applications like voice recognition. It proposes a new algorithm using multiband spectral subtraction and different transform techniques like Haar and Daubechies transforms to remove noise and improve speech quality. The paper compares this approach to existing methods like spectral subtraction and Wiener filtering through various objective measures.
It is a basic ppt of pattern recognition using wavelates and contourlets.... I will describe the algo into next slide... Thank you... It is a good ppt you can learn the basic of this project
Wavelet transform is one of the important methods of compressing image data so that it takes up less memory. Wavelet based compression techniques have advantages such as multi-resolution, scalability and tolerable degradation over other techniques.
Wavelets are mathematical functions. The wavelet transform is a tool that cuts up data, functions or operators into different frequency components and then studies each component with a resolution matched to its scale. It is needed, because analyzing discontinuities and sharp spikes of the signal and applications as image compression, human vision, radar, and earthquake prediction. Wai Mar Lwin | Thinn Aung | Khaing Khaing Wai "Applications of Wavelet Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27958.pdfPaper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/27958/applications-of-wavelet-transform/wai-mar-lwin
It is a basic ppt of pattern recognition using wavelates and contourlets.... I will describe the algo into next slide... Thank you... It is a good ppt you can learn the basic of this project
Wavelet transform is one of the important methods of compressing image data so that it takes up less memory. Wavelet based compression techniques have advantages such as multi-resolution, scalability and tolerable degradation over other techniques.
Wavelets are mathematical functions. The wavelet transform is a tool that cuts up data, functions or operators into different frequency components and then studies each component with a resolution matched to its scale. It is needed, because analyzing discontinuities and sharp spikes of the signal and applications as image compression, human vision, radar, and earthquake prediction. Wai Mar Lwin | Thinn Aung | Khaing Khaing Wai "Applications of Wavelet Transform" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27958.pdfPaper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/27958/applications-of-wavelet-transform/wai-mar-lwin
Architecture of direct_digital_synthesizanjunarayanan
A frequency synthesizer is an electronic device which generates a range of frequency mostly sine wave from a fixed clock provided. These frequency synthesizers are commonly found in radio receivers, mobile telephones radio telephones, wacky talkies, as local oscillators, satellite receivers, GPS system. Direct digital synthesizer is a frequency synthesizer which digitally creates arbitrary wave forms of different frequencies from the fixed frequency provided as clock input.DDS generates digital wave forms and these digital waveforms are converted to analog signals by the digital to analog converter connected at the output of DDS. The DDS designed here is a ROM based DDS. DDS has many advantages over PLL and other similar approaches such as fast settling time, sub-Hertz frequency resolution, continuous phase switching response and low phase noise. This system has many applications such as the confidential message transfer, speedy frequency switching.
Index terms –DDS-Direct Digital Synthesizer
An Optical Time Domain Reflectometer (OTDR) is an important instrument used by organizations to certify the performance of new fiber optics links and detect problems with existing fiber links.
Real-Time Active Noise Cancellation with Simulink and Data Acquisition ToolboxIDES Editor
This paper presents the feasibility of implementing
single channel negative feedback Active Noise Cancellation
technique using adaptive filters in Real-time environment[1].
In order to establish the suitability and credibility of LMS
Algorithm for adaptive filtering in real world scenario, its
efficiency was tested beyond system based ideal simulations.
Within the MATLAB® software environment two different
methods were used to perform Real-time ANC namely
Simulink® and Data Acquisition ToolboxTM. Human voice is
used as test signal. For processing and performing adaptive
filtering, Block LMS Filter was utilised in Simulink and Error
Normalised Step Size algorithm was used in between input
and output of Signals by DAQ (Data Acquisition) toolbox
interface. A general method of using DAQ commands has been
employed which also allows for almost any kind of complex
real-time audio processing and is quite easy to follow.
An optical time-domain reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber. An OTDR is the optical equivalent of an electronic time domain reflectometer. It injects a series of optical pulses into the fiber under test and extracts, from the same end of the fiber, light that is scattered (Rayleigh backscatter) or reflected back from points along the fiber. The scattered or reflected light that is gathered back is used to characterize the optical fiber. This is equivalent to the way that an electronic time-domain meter measures reflections caused by changes in the impedance of the cable under test. The strength of the return pulses is measured and integrated as a function of time, and plotted as a function of fiber length.
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®.
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.
Architecture of direct_digital_synthesizanjunarayanan
A frequency synthesizer is an electronic device which generates a range of frequency mostly sine wave from a fixed clock provided. These frequency synthesizers are commonly found in radio receivers, mobile telephones radio telephones, wacky talkies, as local oscillators, satellite receivers, GPS system. Direct digital synthesizer is a frequency synthesizer which digitally creates arbitrary wave forms of different frequencies from the fixed frequency provided as clock input.DDS generates digital wave forms and these digital waveforms are converted to analog signals by the digital to analog converter connected at the output of DDS. The DDS designed here is a ROM based DDS. DDS has many advantages over PLL and other similar approaches such as fast settling time, sub-Hertz frequency resolution, continuous phase switching response and low phase noise. This system has many applications such as the confidential message transfer, speedy frequency switching.
Index terms –DDS-Direct Digital Synthesizer
An Optical Time Domain Reflectometer (OTDR) is an important instrument used by organizations to certify the performance of new fiber optics links and detect problems with existing fiber links.
Real-Time Active Noise Cancellation with Simulink and Data Acquisition ToolboxIDES Editor
This paper presents the feasibility of implementing
single channel negative feedback Active Noise Cancellation
technique using adaptive filters in Real-time environment[1].
In order to establish the suitability and credibility of LMS
Algorithm for adaptive filtering in real world scenario, its
efficiency was tested beyond system based ideal simulations.
Within the MATLAB® software environment two different
methods were used to perform Real-time ANC namely
Simulink® and Data Acquisition ToolboxTM. Human voice is
used as test signal. For processing and performing adaptive
filtering, Block LMS Filter was utilised in Simulink and Error
Normalised Step Size algorithm was used in between input
and output of Signals by DAQ (Data Acquisition) toolbox
interface. A general method of using DAQ commands has been
employed which also allows for almost any kind of complex
real-time audio processing and is quite easy to follow.
An optical time-domain reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber. An OTDR is the optical equivalent of an electronic time domain reflectometer. It injects a series of optical pulses into the fiber under test and extracts, from the same end of the fiber, light that is scattered (Rayleigh backscatter) or reflected back from points along the fiber. The scattered or reflected light that is gathered back is used to characterize the optical fiber. This is equivalent to the way that an electronic time-domain meter measures reflections caused by changes in the impedance of the cable under test. The strength of the return pulses is measured and integrated as a function of time, and plotted as a function of fiber length.
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®.
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.
Development of Algorithm for Voice Operated Switch for Digital Audio Control ...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Performance enhancement of dct based speaker recognition using wavelet de noi...eSAT Journals
Abstract Presence of noise in the speech signal is one of the major problems in Speaker Recognition. The speaker recognition performance gradually degrades as the intensity of noise increases. The system gives high accuracy when the speech signal is noise free, but in real life scenario getting a noise free speech signal is challenging. Hence, elimination of the noise from speech signal is an important aspect in speaker recognition process. This work uses wavelet based denoising of the recorded speech signal in order to enhance the performance of speaker recognition. In this paper, wavelet based denoising technique has been applied to the DCT based speaker recognition system which was proposed in our previous work. Additive white Gaussian noise has been added to the speech signal and performance analysis of the system has been done using different SNR value. Keywords: Wavelet denoising, AWGN, Speaker Recognition, Thresholding, DCT, Feature Extraction.
As Digital Still Cameras (DSC) become smaller, cheaper and higher in resolution, photographs are increasingly prone to blurring from shaky hands. Optical image stabilization (OIS) is an effective solution that addresses the quality of images, and is an idea that has been around for at least 30 years. It has only recently made its way into the low-cost consumer camera market, and will soon be migrating to the higher end camera phones. This paper provides an overview of common design practices and considerations for optical image stabilization and how silicon-based MEMS dual-axis gyroscopes with their size, cost and performance advantages are enabling this vital function for image capturing devices
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The Silent sound technology is an amazing solution for those who had lost their voice but wish to communicate over the phone. This technology basically allows people to make calls without producing sounds.
This technology basically detect every lip movement and internally converts the electrical pulses into sounds signals and sends them neglecting all other surrounding noise. This report outlines the history associated with this technology presenting the method or techniques used in achieving silent sounds, which are electromyography and Image processing. This research reviews the underlined futures of the technology that immediately transforms into the language of the user's choice but, for the languages like Chinese different tones can hold many different meanings
TWS earphones are also known as True Wireless Stereo (True Wireless Stereo) true wireless stereo. They are mainly connected to mobile phones through Bluetooth modules without wires.
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In audio test for TWS earphone, different noise cancellation solutions bring users a different noise cancellation experience, MegaSig can provide customers and partners with a complete test instrument and system solutions from research and development to mass production.
Single Channel Speech Enhancement using Wiener Filter and Compressive Sensing IJECEIAES
The speech enhancement algorithms are utilized to overcome multiple limitation factors in recent applications such as mobile phone and communication channel. The challenges focus on corrupted speech solution between noise reduction and signal distortion. We used a modified Wiener filter and compressive sensing (CS) to investigate and evaluate the improvement of speech quality. This new method adapted noise estimation and Wiener filter gain function in which to increase weight amplitude spectrum and improve mitigation of interested signals. The CS is then applied using the gradient projection for sparse reconstruction (GPSR) technique as a study system to empirically investigate the interactive effects of the corrupted noise and obtain better perceptual improvement aspects to listener fatigue with noiseless reduction conditions. The proposed algorithm shows an enhancement in testing performance evaluation of objective assessment tests outperform compared to other conventional algorithms at various noise type conditions of 0, 5, 10, 15 dB SNRs. Therefore, the proposed algorithm significantly achieved the speech quality improvement and efficiently obtained higher performance resulting in better noise reduction compare to other conventional algorithms.
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Research work ppt on speech intelligibility quality in telugu speech patterns using a wavelet based hybrid threshold transform method
1. Speech Intelligibility Quality in Telugu Speech Patterns Using a
Wavelet-Based Hybrid Threshold Transform Method
Presentation By
Dr.S.China Venkateswarlu
Professor in Dept. of ECE, IARE(Autonomous)
Co-Authors
Dr. Naluguru Udaya Kumar
Associate Professor in Dept. of ECE, MLRITM
(Autonomous)
Dr.Vallabhuni Vijay
Associate Professor in Dept. of ECE, IARE(Autonomous)
Paper ID: 287
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
3. Abstract
MLRITM
This paper proposes the algorithm of multiband spectral
subtraction in which the quality of speech has enhanced the
intelligibility of the speech.
In Our daily life, speech is significant to convey to destination.
When we consider industrial areas where we face the noise in
speech,
there may be some additional noise added to the signal
which causes the disturbance to the original signal cannot be
achieved perfectly to remove this noise;
different algorithms such as spectral subtraction, wiener filter.
At the same time While these two algorithms have different
objective measure analyses such as SNR , SSNR, MSE,
PSNR, NRMSE, PSEQ.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
4. Abstract
MLRITM
Comparing these values with Multiband spectral subtraction
some objective measures such as
SNR,
Segmental SNR,
Frequency segmental SNR,
Cestrum and overall SNR.
By using different transform techniques such as Haar transform,
Daubechies transform.
Using these transform techniques and algorithms,
we can remove the noise and improve the quality of the speech
signal.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
5. MLRITM ICCSSS 2020
There is no conventional challenge in improving voice production and
eligibility that remains accessible and unresolved;
this has been an active study era for many years.
The demand for communication headsets for industrial use, new
applications such as hands-free networking, and
automatic voice recognition devices have fueled development in this
subject.
Non-steady noise is one of the major issues for the modern state of
technology.
Standard algorithms can detect non-stationary noise, but output
efficiency is decreased as background noise status is increased
Hence, algorithms to increase speech communication efficiency in the
industrial and heavily noisy world improve speech communication.
Introduction
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
6. Introduction
MLRITM
There is no conventional challenge in improving voice production and
eligibility that remains accessible and unresolved;
This has been an active study era for many years.
The demand for communication headsets for industrial use, new
applications such as hands-free networking, and
automatic voice recognition devices have fueled development in this
subject.
Non-steady noise is one of the major issues for the modern state of
technology.
Standard algorithms can detect non-stationary noise, but output
efficiency is decreased as background noise status is increased
Hence,
algorithms to increase speech communication efficiency in the
industrial and heavily noisy world improve speech communication.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
7. Presentation Outline
MLRITM
Coefficient thresholding methods such as binary masking and transformation of the
wavelet have also been used widely to improve expression.
Modulation channel methods have performed little work.
Yet the optimum performance in speech efficiency and intelligibility could not be
achieved in both strategies.
In other words, it is difficult to find effective ways to restrict the sounds of low SNR.
Previous approaches had limitations, including the incorporation of peaks (musical
noise), more iterations, and low speech efficiency and intelligibility.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
8. Speech Processing
MLRITM
Speech processing refers to the analysis of speech signals as well as signal
processing techniques.
There are some techniques which are used in the speech processing and those are:
Dynamic Time Warping is an algorithm for the simulation measurement between the
two time series.
Generally speaking, the digital time warping equation determines the ideal fit
between two sequences with some restrictions and laws like time series.
The optimum match which meets all constraints and laws and which has the minimum
cost in which the cost is calculated as the number of absolute differences between
their values for each matched index pair.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
9. Speech processing
MLRITM
Artificial Neural Networks are built on an artificial neuronal-like set of linked units or
nodes that model the biological brain loose.
Each communication can relay a signal from one artificial neuron to another,
like Here the main point included is the algorithms are made with the help of
programming.
And the next one is Signal processing. Speech processing is mainly regarded as a
special case of digital signal processing.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
10. De-Noising Techniques
MLRITM
The de-noising of speech is an issue of many years.
Due to an input noisy signal, we want to filter the unwanted, damaging the interest
signal.
You can visualize someone speaking during a video call when a music piece is playing
behind the scenes.
In this case, it is the responsibility of the speech denotation device to remove the
background noise to increase the speech signal.
[1] This technology is particularly important for video and audio conferencing where
noise can greatly reduce speech intelligibility, in addition to many others.
Denoting or estimating functions requires as much as possible reconstruction of the
signal on the basis of measurements of a useful noise corrupting signal.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
11. De-Noising Techniques
MLRITM
The below shown is the pictorial presentation of De-Noising
using wavelet transform.
And first it starts with an Input Noisy Speech pattern. So here we
will input noise consisting of speech patterns.
And then it is passed to wavelet transform to check for it.
ICISSC-2021
Figure 1:.De-noising using wavelet transform.
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
12. De-Noising Techniques
MLRITM
And then to estimate the threshold to check the
estimation of this.
And then there will be two ways. One will be soft
thresholding and other will be hard thresholding. So we can
apply either of them to proceed. Then next it goes to
inverse wavelet transform and then finally its upto denoised
speech.
Here we can see that the speech is passing and the
noise which is alongside speech is removed. Hence this is
the process of de noising technique
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
13. WAVELET TRANSFORMS
MLRITM
In several areas of mechanics, engineers, seismography, electronic
data processing, etc., wavelet transformations are widely used in the
analyses, encoded and reconstructions of signals.
In maths, an orthonormal series generated by a wavelet is a
representation of square integrated (real or compound-evaluated)
function.
This article includes a systematic, mathematical description of the
transformation of an orthonormal wavelet.
The basic theory of wavelet transformations is that the conversion can
accommodate only time extension shifts, but not shape.
This is influenced by the selection of appropriate basic functions.
Changes in the period extended should be in accordance with the
preceding base function analysis frequency.
ICISSC-2021
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
14. Presentation Outline
MLRITM
The Fourier Transformations main drawback is that it absorbs
global frequency detail,, which means frequencies that last for an
entire signal.
Not all applications, where the pulse has small intervals of
characteristic oscillation, are suitable for that kind of signal
decomposition.
The wavelet transform, a feature that breaks into a series of
wavelets, is an alternative solution.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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15. DIFFERENT WAVELET TRANSFORMS
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HAAR:
The first and shortest wavelet is the Haar wavelet, which is the starting point for any
discussion of wavelets.
The Haar wavelet is a step-like feature that is discontinuous. It depicts the same
wavelet as the Daubechies db1 wavelet.
DAUBECHIES: Ingrid Daubechies, perhaps the most splendid star in the realm of
wavelet research, developed what are called minimally upheld orthonormal wavelets-
along these lines making discrete wavelet investigation practicable.
The names of the Daubechies family wavelets are written in db N, where N is the
order, and db is the "last name" of the wavelet.
SYMLETS: Daubechies offered symlets as modifications to the db family as almost
balanced wavelets. The two wavelet families' properties are compared. The wavelet
abilities in psi are listed below.
By assembling waveinfo('sym') from the MATLAB command line, we may acquire an
overview of the fundamental attributes of this family.
MORLET: There is no scaling function in this wavelet, but it is explicit.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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16. LITERATURE SURVEY
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For adaptive noise cancellation, Widrow et al. have suggested wiener filters based on
both noise and voice.
A complicated continuum of clean speech (time domain form) is easily retrieved using
the linear model between observed and projected signal.
Clean voice spectral amplitude is measured in the Wiener filter and also phase is
retrieved directly from the noisy sign.
This approach DSP Digital signal processor is suitable for stationary applications
where the ideal Wiener filter reduces the measurement error.
In the Wiener filter a further number of iterations were carried out.
NOISY SPEECH ENHANCEMENT
The problem of noise reduction in the paper was that the optimal signal y(n) is
recovered with the cleansing speech signal x(n) and n is the discrete time indicator of 0.
(n) = x(n) + s(n) (2.1)
Where S(n) is a background noise, and x(n) is background information that is not
correlated. Non-stationary signals are used as input data for the analysis and
implementation of the proposed method.
In the frequency domain short-time, Fourier transformations are used to calculate the
clean language patterns provided by noisy speech patents.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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17. NOISY SPEECH ENHANCEMENT
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Y(k,m) = S(k,m) + U(k,m) (2.2)
Where Y(k,m), S(k,m) and U(k,m), the frequency bin k is belongs to {0,
1, 2, 3, …., k-1} and the time frame m, respectively the STFTs of y(n),
S(n) and u(n).
The variance of Y(k,m) is necessary for further study of the threshold
because of s(n) and u(n) uncorrected by inference.
Recently, improving expression has become an essential component
of speech coding and speech recognition technologies.
Speech amplification has two main considerations :
Language evaluation and noise power assessment.
The voice estimation is based on the mathematical speech model,
the distortion criterion and the measured noise
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Dr.S.China Venkateswarlu-Professor of ECE ,
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18. EXISTING METHOD
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From the paper we had, the existing model has both soft and hard
thresholding.
So this causes some advantages and also some disadvantages.
To solve some of the disadvantages, here is the mix of soft and hard
thresholding and that is called the Hybrid threshold.
HYBRID THRESHOLD: As few drawbacks are present in Soft
Thresholding and Hard Thresholding individually during noise reduction
methods.
To come back with these drawbacks we use the combination of these
soft and hard thresholding techniques to get a new kind of thresholding
technique and it is the Hybrid Thresholding Method.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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19. HYBRID THRESHOLD
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In soft Thresholding methods it removes the discontinuity of the
signal and in hard thresholding technique the discontinuity of the signal
presents.
Sometimes it keeps and sometimes it kills the procedure and is more
and more often.
So these combined techniques are used for developing a new
technique which is more efficient in manner and it is named Hybrid
Thresholding. Below
Figure is Block Diagram of Spectral Subtraction Method
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Dr.S.China Venkateswarlu-Professor of ECE ,
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20. Presentation Outline
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Here the input is through Windowing and then it passes
through FFT which is a fast fourier transform.
And then these speech signals will go to noise estimation and
then to spectral subtraction.
Here these speech signals then continue to Complex Spectrum
and then to IFFT which is Inverse Fast Fourier Transform (IFFT) .
Here then continues to Overlap – add and then to Enhanced
Speech Signal. Here we get a clear signal without noise.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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21. PRINCIPLE OF SPECTRAL SUBTRACTION METHOD
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Take a noise signal having noises which is derived from the
independent additive noises as
y[n] = s[n] + d[n]
where y[n] ~~~ sampled noisy speech,
s[n] ~~~ clean speech,
d[n] ~~~ additive noise
Is considered additive noise with a zero mean and no relevance to any
type of clear speech. Due to the non-stationary and time-variable
nature of speech signals,.
Its representation is in the Short -Time Fourier Transform, which has
the following transformation.
Y (ω, k) = S(ω, k) + D(ω, k) (2.3)
By removing a noise estimate from the received signal,
the speech can be approximated.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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22. PRINCIPLE OF SPECTRAL SUBTRACTION METHOD
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Averaging recent speech pause frames yields an estimate of the noise spectrum:
(2.5)
M is the number of consecutive frames.
The spectral subtraction is considered as filter by manipulating , so that it can be
given as the product of noisy speech spectrum along with spectral subtraction filter
(SSF) as below:
(2.6)
where H(ω) is gain function and also known as Spectral Subtraction Filter (SSF).
The H(ω) is a zero phase filter, having magnitude response as in between 0 ≤
H(ω) ≤ 1.
(2.7)
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Dr.S.China Venkateswarlu-Professor of ECE ,
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23. PRINCIPLE OF SPECTRAL SUBTRACTION METHOD
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To construct back the previous signal, we need phase estimation of the speech.
Thus, the speech signal in a frame is calculated by
(2.8)
Those estimated speech signals recover in the domain of time and inverse Fourier
transforming S(ω) using the Overlap-add technique.
Although this Spectral Subtraction method reduces the majority of the noises and it
also has some drawbacks like it depends more.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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24. Hybrid Thresholding
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Here we can see that the flow chart starts
with the Noisy Speech signal. In other words,
the noise is added to the speech signal.
And then it continues to Time-Frequency
Analysis in Windowing mode. And next to
Wavelet Decomposition passing through it.
Here the wavelet is decomposed. Next is
Hybrid Thresholding, as said above, this is the
mixture of Soft and hard thresholding.
The signal passes through this. And next to
IWT, which is Integer Wavelet Transform. And
then finally to enhance speech signal.
Here enhanced meaning, the noise which is
present with the signal is eradicated. And the
pure, enhanced signal is shown.
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Figure 2.2 : Hybrid thresholding
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Dr.S.China Venkateswarlu-Professor of ECE ,
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25. Wiener Filter
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The Wiener Filter (WF) is a kind of filter that reduces the mean square error (MSE). The GF –
Gain Function of WF Wiener(ω), is written in the form of the power spectral density (PSD) of
clean speech of the noise Pd (ω)
(2.9)
The fixed gain (FG) at every frequency levels and their requirements to estimate the PSD of
the clean signal and noise are before filtering This is the drawback of Wiener Filter.
So we use adaptive WF to round the approximation of WF gain function.
(2.10)
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Dr.S.China Venkateswarlu-Professor of ECE ,
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26. Presentation Outline
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Problem Identification
During communication between two people in a laboratory, there is some
noise added to the original speech signals of the speaker. This noise may be due to
Environmental disturbance or may be people around the speakers who create the
disturbance. This result in the quality of speech is degraded and cannot transmit the
message in effective manner. This is the problem which we face in our daily life
situations. In order to rectify the problem, we have the algorithm which enhances the
speech quality.[6].
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Dr.S.China Venkateswarlu-Professor of ECE ,
IARE-Hyderabad
27. Presentation Outline
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PROPOSED METHOD
Multiband spectral subtraction is an Algorithm which removes the noise in speech
signals by using different transform techniques. In real life situations whenever there is
transmission of information we add a carrier signal to the message signal in order to
travel more distance .Also we add some noise in the transmitter side and remove that
noise in the receiver side before we retrieve actual information.[6] But sometimes due to
environmental issues this noise will not be discarded completely and cause
disturbances. So in order to remove this noise we are using this algorithm and using
different wavelet transform techniques such Haar ,Daubechies etc and we are denoising
the speech signals .There are some thresholding techniques such as hard thresholding
and soft thresholding.[13]
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Dr.S.China Venkateswarlu-Professor of ECE ,
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28. Multiband spectral subtraction algorithm
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The assumption behind the
multiband spectral subtraction
method is that the added noise
will be stationary and
uncorrelated with a clean voice
signal.
Figure 3.1 : Hybrid Threshold
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Dr.S.China Venkateswarlu-Professor of ECE ,
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30. Noise Estimation
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Here we can see that the process starts with Noise estimation and
next to windowing along with FFT.
This process continues to Phase and then meanwhile to Multi-Band.
These all together pass to Power spectral Modification and then to IFFT.
Noise Estimation: In real life situations, noise does not affect the
speech signal uniformly over the whole spectrum.
Few frequencies will affect these speeches. This kind of noise is
known as stationary noise. These estimated noise in the speech signals
can be removed by using the special algorithms.
One such algorithm we are implementing in this paper.
From this we can calculate the signal to noise ratio and some objective
measures.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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31. Results and Discussions
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When we simulate in the
matlab software by using
the speech corpus
databases as input,
we get the output signal
with noise and original
noise.
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Figure : Original Signal and Denoised Signals
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Dr.S.China Venkateswarlu-Professor of ECE ,
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32. Results and Discussions
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above Figure : Original Signal, After De-noising the signal with noise we get the original signal
as shown .
When we give a speech corpus05 database as an input ,
when we analyze with a symlet then we get the above threshold coefficients and original
coefficients.
Left side graphs represent the absolute values of the coefficients.
In this we use threshold transform method
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Figure : Original Signal and De-noised Signals
9/28/2021
Dr.S.China Venkateswarlu-Professor of ECE ,
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33. Results and Discussions
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above Figure : Original Signal, After De-noising the signal with noise we get the original signal
as shown .
When we give a speech corpus05 database as an input ,
when we analyze with a symlet then we get the above threshold coefficients and original
coefficients.
Left side graphs represent the absolute values of the coefficients.
In this we use threshold transform method
ICISSC-2021
Figure : Original Signal and De-noised Signals
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Dr.S.China Venkateswarlu-Professor of ECE ,
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36. CONCLUSION AND FUTURE SCOPE
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The hybrid threshold approach was developed to improve the efficiency of speech
communication Systems in a noisy industrial setting.
On a shop floor, where employees are interacting with one another,
any loss of speech is unacceptable.
For this reason,
we present a technique in which non-stationary noise enhancement methods are
combined with evolutionary computation for machine learning with the aim of
improving distorted speech in the audio logical phase.
Some variables are more important for speech enhancement algorithms than others,
depending on the needs of shop floor staff.
For the best Wavelet transform range,
a hybrid thresholding scheme for non-stationary low SNR noisy speech patterns is
considered in this paper. In hybrid thresholding,
the optimal selection of decomposition levels in the wavelet transform is more
critical for speech quality and intelligibility
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Dr.S.China Venkateswarlu-Professor of ECE ,
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37. CONCLUSION AND FUTURE SCOPE
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As compared to traditional approaches, this hybrid approach outperforms them in
terms of calculating parameters such as
SNR,
SSNR,
MSE,
PSNR,
NRMSE and PSEQ for speech content and intelligence.
Spectrograms for extremely non-stationary noises with negative SNR show a
significant improvement in the proposed approach when the above mentioned
parameters are used.
The proposed method used a simple
haar wavelet,
Daubechies, to solve the problem of denoising at various SNR decibel levels.
In comparison to traditional approaches,
the hybrid device performs better in the experimental analysis,
which requires both parameters and spectrograms.
Here we use multiband spectral subtraction method to enhance the signal.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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38. CONCLUSION AND FUTURE SCOPE
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This work can be extended in order to increase the accuracy and intelligibility of the
speech processing in communication.
For the implementation of speech processing in the Digital signal processors for the
faster transmission of information between the source and destination.
Here,
even if we use multiband spectral subtraction method to enhance the signal,
we can also use wiener filter with the help of this as this can help.
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Dr.S.China Venkateswarlu-Professor of ECE ,
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39. REFERENCES
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[1] Hirsch, H. G., & Pearce, D. (2000). The Aurora experimental framework for the performance
evaluation of speech recognition systems under noisy conditions. In ISCA ITRW
ASR2000, Paris, France, 18–20, 2000.
[2] Hamid, M. E., Molla, M. K. I., Dang, X., & Nakai, T. (2013). Single channel speech enhancement
using adaptive soft-thresholding with bivariate EMD. ISRN Signal Processing,
2013(2013), 1–9.
[3] Singh, S., & Mutawa, A. M. (2016). A wavelet based transform method for quality improvement
in noisy speech patterns of Arabic Language. International Journal of Speech Technology, 18(2),
157–166.
[4] Farouk, M. H. (2018). Application of wavelets in speech processing. Springer briefs in speech
technology (2nd ed.). Springer.
[5] Polikar, R. (1996). The wavelet tutorial. [Internet] [Cited 2017 March 30].
[6] Kaur, H., & Talwar, R. (2015). Overlapping frame approach to estimate and reduce noise from
single channel speech. International Journal of Signal Processing, Image Processing and
Pattern Recognition, 8(4), 49–58.
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40. REFERENCES
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[7] D Manikanta, S.China Venkateswarlu “Performance in Denser Networks Using IoT Adaptive
Configurations' European Journal of Molecular & Clinical Medicine, Volume-8, Issue 01
2021,pp.1664-1686, Publisher-European Journal of Molecular & Clinical Medicine:
[8] S.China Venkateswarlu,Ch.Sashi Kiran, R.V.Santhosh Nayan, Vijay Vallabhuni, P.Ashok Babu,
V.Siva Nagaraju "Artificial Intelligence Based Smart Home Automation System Using Internet of
Things,, Publication date:2 021/9, Patent Office-India, Application number: 202041057023.
[9] S.China Venkateswarlu, Naluguru Udaya Kumar, Annam Karthik , "Wavelet Region implanting
watermark upgrades the security framework in Digital Speech Watermarking 2021/3/10, IOP
Conference Series: Materials Science and Engineering,volume- 12013,issue - ICCSSS 2020
[10] S.China Venkateswarlu, Udaya Kumar Naluguru, A Karthik, "Speech Enhancement Using
Recursive Least Square Based on Real-time adaptive filtering algorithm" 2021 IEEE-6th
International Conference for Convergence in Technology (I2CT),pp no. 1-6.
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41. MLRITM
Department of ECE 41
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