The document proposes a noise reduction technique for speech signals in wireless LAN using a linear prediction error filter (LPEF) and adaptive digital filter (ADF). It aims to improve the signal-to-noise ratio. The LPEF is used to predict the speech signal and generate a prediction error signal. The ADF then reconstructs and subtracts the background noise from the error signal to extract the speech. Additionally, the document demonstrates that wideband MRI can obtain images with quality identical to conventional MRI in terms of SNR. It involves simultaneously exciting and acquiring multiple slices using a wideband signal.
Sending multimedia data (e.g. data, image and
video) using Power Line Communications (PLC) has been growing
significantly. In fact, impulsive noise (IN) causes significant
degradation which restricts communication performance in PLC.
In this paper, we propose to study the impact of IN on image
communication in Orthogonal Frequency Division Multiplexing
(OFDM) based PLC. A strightforward conventional iterative
IN reduction algorithm, presented in [9] can be applied to
improve bit-error rate (BER) in OFDM system. But, this iterative
algorithm is insufficient to improve the visual quality Peak Signalto-
Noise Ratio (PSNR) performance in image communication.
First, we study its performance in terms of BER and PSNR using
a convolutional encoder (CE). Then, we modify its concept by
introducing CE and Viterbi decoder into the iterative algorithm.
Finally, we aim to show the impact of BER degradation on
visual quality PSNR performance of the reconstructed image.
Our results lay out that the proposed method provides good
PSNR and visual quality improvement than the conventional
algorithm with and without CE.
This digital method is built using chirp z-transform(CZT) and provides 100% alias-free bandwidth such as using ideal LPF. This noble method is efficient for economic and practical considerations.
Speech is the vocalizer form of human communication,and based upon the syntactic combination of lexical and vocabularies. The aim of speech coding is to compress the speech signal to the highest possible compression ratio bu t maintaining user acceptability.There are many methods for speech compression like Linear Pre dictive coding (LPC),Code Excited Linear Predictive coding (CELP),Sub-band coding,T ransform coding:- Fast Fourier Transform (FFT),Discrete Cosine Transform (DCT),Continuous Wavelet Transform (CWT),Discrete Wavelet Transform (DWT),Variance Fractal Compression (VFC),Discrete Cosine Transform (DCT),Psychoacoustics and etc. Few of them are discus in this paper.
Sending multimedia data (e.g. data, image and
video) using Power Line Communications (PLC) has been growing
significantly. In fact, impulsive noise (IN) causes significant
degradation which restricts communication performance in PLC.
In this paper, we propose to study the impact of IN on image
communication in Orthogonal Frequency Division Multiplexing
(OFDM) based PLC. A strightforward conventional iterative
IN reduction algorithm, presented in [9] can be applied to
improve bit-error rate (BER) in OFDM system. But, this iterative
algorithm is insufficient to improve the visual quality Peak Signalto-
Noise Ratio (PSNR) performance in image communication.
First, we study its performance in terms of BER and PSNR using
a convolutional encoder (CE). Then, we modify its concept by
introducing CE and Viterbi decoder into the iterative algorithm.
Finally, we aim to show the impact of BER degradation on
visual quality PSNR performance of the reconstructed image.
Our results lay out that the proposed method provides good
PSNR and visual quality improvement than the conventional
algorithm with and without CE.
This digital method is built using chirp z-transform(CZT) and provides 100% alias-free bandwidth such as using ideal LPF. This noble method is efficient for economic and practical considerations.
Speech is the vocalizer form of human communication,and based upon the syntactic combination of lexical and vocabularies. The aim of speech coding is to compress the speech signal to the highest possible compression ratio bu t maintaining user acceptability.There are many methods for speech compression like Linear Pre dictive coding (LPC),Code Excited Linear Predictive coding (CELP),Sub-band coding,T ransform coding:- Fast Fourier Transform (FFT),Discrete Cosine Transform (DCT),Continuous Wavelet Transform (CWT),Discrete Wavelet Transform (DWT),Variance Fractal Compression (VFC),Discrete Cosine Transform (DCT),Psychoacoustics and etc. Few of them are discus in this paper.
Speech enhancement using spectral subtraction technique with minimized cross ...eSAT Journals
Abstract The aim of speech enhancement is to get significant reduction of noise and enhanced speech from noisy speech. There are several
approaches for speech enhancement .earlier approaches didn’t consider cross spectral terms into account. Cross spectral terms
become prominent when processing window size becomes small i.e. 20ms-30ms. In this paper, an enhancement method is
proposed for significant reduction of noise, and improvement in the quality and perceptibility of speech degraded by correlated
additive background noise. The proposed method is based on the spectral subtraction technique. The simple spectral subtraction
technique results in poor reduction of noise. One of the main reasons for this is neglecting the cross spectral terms of speech and
noise, based on the appropriation that clean speech and noise signals are completely uncorrelated to each other, which is not true
on short time basis. In this paper an improvement in reduction of the noise is achieved as compared to the earlier methods. This
fact is mainly attributed to the cross spectral terms between speech and noise. This algorithm can be implemented and used in
hearing aids for the benefit of hearing impaired people. Objective speech quality measures, spectrogram analyses and subjective
listening tests conforms the proposed method is more effective in comparison with earlier speech enhancement techniques.
Keywords: Spectral Subtaction,Cross Spectral Components
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Research on VoIP Acoustic Echo Cancelation Algorithm Based on SpeexTELKOMNIKA JOURNAL
Echo cancellation has been a major problem to be solved in VoIP, although the integrated echo cancellation module in Speex, it does not consider thread synchronization issues. The frequency domain echo cancellation algorithm MDF of speex is analyzed, and then a synchronization method of playing thread and recording thread is proposed. The results show that the acoustic echo canceller which achieved by the proposed method meet the requirements of voice communication, implementation is easier and therefore provides a reference for the VoIP voice communication and mobile communication terminal.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Analysis of PEAQ Model using Wavelet Decomposition Techniquesidescitation
Digital broadcasting, internet audio and music database make use of audio
compression and coding techniques to reduce high quality audio signal without impairing its
perceptual quality. Audio signal compression is the lossy compression
technique, It
converts original converting audio signal into compressed bitstream. The compressed audio
bitstream is decoded at the decoder to produce a close approximation of the original signal.
For the purpose of improving the coding this work attempts to verify the perceptual
evaluation of audio quality (PEAQ) model in BS.1387 using wavelet decomposition
techniques. Finally the comparison of masking threshold for sub-bands using Wavelet
techniques and Fast Fourier transform (FFT) will be done
Speech Enhancement Using Spectral Flatness Measure Based Spectral SubtractionIOSRJVSP
This paper is aimed to reduce background noise introduced in speech signal during capture, storage, transmission and processing using Spectral Subtraction algorithm. To consider the fact that colored noise corrupts the speech signal non-uniformly over different frequency bands, Multi-Band Spectral Subtraction (MBSS) approach is exploited wherein amount of noise subtracted from noisy speech signal is decided by a weighting factor. Choice of optimal values of weights decides the performance of the speech enhancement system. In this paper weights are decided based on SFM (Spectral Flatness Measure) than conventional SNR (Signal to Noise Ratio) based rule. Since SFM is able to provide true distinction between speech signal and noise signal. Spectrogram, Mean Opinion Score show that speech enhanced from proposed SFM based MBSS possess better perceptual quality and improved intelligibility than existing SNR based MBSS
In this paper, the performances of adaptive noise cancelling system employing Least Mean Square (LMS) algorithm are studied considering both white Gaussian noise (Case 1) and colored noise (Case 2)
situations. Performance is analysed with varying number of iterations, Signal to Noise Ratio (SNR) and tap size with considering Mean Square Error (MSE) as the performance measurement criteria. Results show that the noise reduction is better as well as convergence speed is faster for Case 2 as compared with Case 1. It is also observed that MSE decreases with increasing SNR with relatively faster decrease of MSE in Case 2 as compared with Case 1, and on average MSE increases linearly with increasing number of filter
coefficients for both type of noise situations. All the experiments have been done using computer
simulations implemented on MATLAB platform.
A Novel Uncertainty Parameter SR ( Signal to Residual Spectrum Ratio ) Evalua...sipij
Usually, hearing impaired people use hearing aids which are implemented with speech enhancement
algorithms. Estimation of speech and estimation of nose are the components in single channel speech
enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power
spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses
and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech
enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual
aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty
parameter introduced for the benefits of hearing impaired people in non stationary environments to control
distortions. By estimation and updating of noise based on division of original pure signal into three parts
such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different
values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed
method will compared with any one method WAT(Weighted Average Technique) Hence by using
parameters SR (signal to residual spectrum ratio) and LLR (log like hood ratio), MMSE (Minim Mean
Square Error) in terms of segmented SNR and LLR.
Cancellation of Noise from Speech Signal using Voice Activity Detection Metho...ijsrd.com
Speech Enhancement by suppressing uncorrelated acoustically added noise has been a challenging topic of research for many years. These are the primary choice for real time applications due to the simplicity and comparatively low computational load. This paper shows VAD (Voice activity detection) technique that can detect the non speech segment from the speech signal. It is also shown that it can work powerfully in an unpredictable noise ambience. The technique is mostly done in microprocessors or DSP processors because of their flexibility. But there are several advantages of FPGA over DSP processors like high cost per logic element related to these processors makes them improper for large scale use. From the experimental results, VAD method is implemented on the FPGA chip.
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®.
Implementation of Wide Band Frequency Synthesizer Base on DFS (Digital Frequ...IJMER
Wide Band Frequency Synthesizer has become essential components in wireless communication
systems. They are used as frequency synthesizers with precise and convenient digital control in both traditional
electronics, such as televisions and AM/FM radios, and modern consumer products among which cellular
mobile phone is a striking example.
IC fabrication technology advances have made monolithic integration possible. More and more
electronic devices can be put on the same chip to reduce the number of external components and then the costs.
Therefore, on a single chip we can accomplish many functions for which we might need to make several chips
work together a few years ago. A monolithic wide-band PLL is of great interests to wireless communication
applications due to both its low cost and convenience to switch between different communication standards.
The focus of this work is to implement a wide-band Frequency Synthesizer using as few as possible building
blocks and also as simple as possible structure.
Speech Recognition Systems(SRS) have been implemented by various processors including the digital signal processors(DSPs) and field programmable gate arrays(FPGAs) and their performance has been reported in literature. The fundamental purpose of speech is communication, i.e., the transmission of messages.In the case of speech, the fundamental analog form of the message is an acoustic waveform, which we call the speech signal. Speech signals can be converted to an electrical waveform by a microphone, further manipulated by both analog and digital signal processing, and then converted back to acoustic form by a loudspeaker, a telephone handset or headphone, as desired.The recognition of speech requires feature extraction and classification. The systems that use speech as input require a microcontroller to carry out the desired actions. In this paper, Cypress Programmable System on Chip (PSoC) has been studied and used for implementation of SRS. From all the available PSoCs, PSoC5 containing ARM Cortex-M3 as its CPU is used. The noise signals are firstly nullified from the speech signals using LogMMSE filtering. These signals are then sent to the PSoC5 wherein the speech is recognized and desired actions are performed.
Improvement of minimum tracking in Minimum Statistics noise estimation methodCSCJournals
Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper we propose a new method for minimum tracking in Minimum Statistics (MS) noise estimation method. This noise estimation algorithm is proposed for highly nonstationary noise environments. This was confirmed with formal listening tests which indicated that the proposed noise estimation algorithm when integrated in speech enhancement was preferred over other noise estimation algorithms.
Speech enhancement using spectral subtraction technique with minimized cross ...eSAT Journals
Abstract The aim of speech enhancement is to get significant reduction of noise and enhanced speech from noisy speech. There are several
approaches for speech enhancement .earlier approaches didn’t consider cross spectral terms into account. Cross spectral terms
become prominent when processing window size becomes small i.e. 20ms-30ms. In this paper, an enhancement method is
proposed for significant reduction of noise, and improvement in the quality and perceptibility of speech degraded by correlated
additive background noise. The proposed method is based on the spectral subtraction technique. The simple spectral subtraction
technique results in poor reduction of noise. One of the main reasons for this is neglecting the cross spectral terms of speech and
noise, based on the appropriation that clean speech and noise signals are completely uncorrelated to each other, which is not true
on short time basis. In this paper an improvement in reduction of the noise is achieved as compared to the earlier methods. This
fact is mainly attributed to the cross spectral terms between speech and noise. This algorithm can be implemented and used in
hearing aids for the benefit of hearing impaired people. Objective speech quality measures, spectrogram analyses and subjective
listening tests conforms the proposed method is more effective in comparison with earlier speech enhancement techniques.
Keywords: Spectral Subtaction,Cross Spectral Components
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Research on VoIP Acoustic Echo Cancelation Algorithm Based on SpeexTELKOMNIKA JOURNAL
Echo cancellation has been a major problem to be solved in VoIP, although the integrated echo cancellation module in Speex, it does not consider thread synchronization issues. The frequency domain echo cancellation algorithm MDF of speex is analyzed, and then a synchronization method of playing thread and recording thread is proposed. The results show that the acoustic echo canceller which achieved by the proposed method meet the requirements of voice communication, implementation is easier and therefore provides a reference for the VoIP voice communication and mobile communication terminal.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Analysis of PEAQ Model using Wavelet Decomposition Techniquesidescitation
Digital broadcasting, internet audio and music database make use of audio
compression and coding techniques to reduce high quality audio signal without impairing its
perceptual quality. Audio signal compression is the lossy compression
technique, It
converts original converting audio signal into compressed bitstream. The compressed audio
bitstream is decoded at the decoder to produce a close approximation of the original signal.
For the purpose of improving the coding this work attempts to verify the perceptual
evaluation of audio quality (PEAQ) model in BS.1387 using wavelet decomposition
techniques. Finally the comparison of masking threshold for sub-bands using Wavelet
techniques and Fast Fourier transform (FFT) will be done
Speech Enhancement Using Spectral Flatness Measure Based Spectral SubtractionIOSRJVSP
This paper is aimed to reduce background noise introduced in speech signal during capture, storage, transmission and processing using Spectral Subtraction algorithm. To consider the fact that colored noise corrupts the speech signal non-uniformly over different frequency bands, Multi-Band Spectral Subtraction (MBSS) approach is exploited wherein amount of noise subtracted from noisy speech signal is decided by a weighting factor. Choice of optimal values of weights decides the performance of the speech enhancement system. In this paper weights are decided based on SFM (Spectral Flatness Measure) than conventional SNR (Signal to Noise Ratio) based rule. Since SFM is able to provide true distinction between speech signal and noise signal. Spectrogram, Mean Opinion Score show that speech enhanced from proposed SFM based MBSS possess better perceptual quality and improved intelligibility than existing SNR based MBSS
In this paper, the performances of adaptive noise cancelling system employing Least Mean Square (LMS) algorithm are studied considering both white Gaussian noise (Case 1) and colored noise (Case 2)
situations. Performance is analysed with varying number of iterations, Signal to Noise Ratio (SNR) and tap size with considering Mean Square Error (MSE) as the performance measurement criteria. Results show that the noise reduction is better as well as convergence speed is faster for Case 2 as compared with Case 1. It is also observed that MSE decreases with increasing SNR with relatively faster decrease of MSE in Case 2 as compared with Case 1, and on average MSE increases linearly with increasing number of filter
coefficients for both type of noise situations. All the experiments have been done using computer
simulations implemented on MATLAB platform.
A Novel Uncertainty Parameter SR ( Signal to Residual Spectrum Ratio ) Evalua...sipij
Usually, hearing impaired people use hearing aids which are implemented with speech enhancement
algorithms. Estimation of speech and estimation of nose are the components in single channel speech
enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power
spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses
and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech
enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual
aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty
parameter introduced for the benefits of hearing impaired people in non stationary environments to control
distortions. By estimation and updating of noise based on division of original pure signal into three parts
such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different
values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed
method will compared with any one method WAT(Weighted Average Technique) Hence by using
parameters SR (signal to residual spectrum ratio) and LLR (log like hood ratio), MMSE (Minim Mean
Square Error) in terms of segmented SNR and LLR.
Cancellation of Noise from Speech Signal using Voice Activity Detection Metho...ijsrd.com
Speech Enhancement by suppressing uncorrelated acoustically added noise has been a challenging topic of research for many years. These are the primary choice for real time applications due to the simplicity and comparatively low computational load. This paper shows VAD (Voice activity detection) technique that can detect the non speech segment from the speech signal. It is also shown that it can work powerfully in an unpredictable noise ambience. The technique is mostly done in microprocessors or DSP processors because of their flexibility. But there are several advantages of FPGA over DSP processors like high cost per logic element related to these processors makes them improper for large scale use. From the experimental results, VAD method is implemented on the FPGA chip.
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®.
Implementation of Wide Band Frequency Synthesizer Base on DFS (Digital Frequ...IJMER
Wide Band Frequency Synthesizer has become essential components in wireless communication
systems. They are used as frequency synthesizers with precise and convenient digital control in both traditional
electronics, such as televisions and AM/FM radios, and modern consumer products among which cellular
mobile phone is a striking example.
IC fabrication technology advances have made monolithic integration possible. More and more
electronic devices can be put on the same chip to reduce the number of external components and then the costs.
Therefore, on a single chip we can accomplish many functions for which we might need to make several chips
work together a few years ago. A monolithic wide-band PLL is of great interests to wireless communication
applications due to both its low cost and convenience to switch between different communication standards.
The focus of this work is to implement a wide-band Frequency Synthesizer using as few as possible building
blocks and also as simple as possible structure.
Speech Recognition Systems(SRS) have been implemented by various processors including the digital signal processors(DSPs) and field programmable gate arrays(FPGAs) and their performance has been reported in literature. The fundamental purpose of speech is communication, i.e., the transmission of messages.In the case of speech, the fundamental analog form of the message is an acoustic waveform, which we call the speech signal. Speech signals can be converted to an electrical waveform by a microphone, further manipulated by both analog and digital signal processing, and then converted back to acoustic form by a loudspeaker, a telephone handset or headphone, as desired.The recognition of speech requires feature extraction and classification. The systems that use speech as input require a microcontroller to carry out the desired actions. In this paper, Cypress Programmable System on Chip (PSoC) has been studied and used for implementation of SRS. From all the available PSoCs, PSoC5 containing ARM Cortex-M3 as its CPU is used. The noise signals are firstly nullified from the speech signals using LogMMSE filtering. These signals are then sent to the PSoC5 wherein the speech is recognized and desired actions are performed.
Improvement of minimum tracking in Minimum Statistics noise estimation methodCSCJournals
Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper we propose a new method for minimum tracking in Minimum Statistics (MS) noise estimation method. This noise estimation algorithm is proposed for highly nonstationary noise environments. This was confirmed with formal listening tests which indicated that the proposed noise estimation algorithm when integrated in speech enhancement was preferred over other noise estimation algorithms.
P ERFORMANCE A NALYSIS O F A DAPTIVE N OISE C ANCELLER E MPLOYING N LMS A LG...ijwmn
n voice communication systems, noise cancellation
using adaptive digital filter is a renowned techniq
ue
for extracting desired speech signal through elimin
ating noise from the speech signal corrupted by noi
se.
In this paper, the performance of adaptive noise ca
nceller of Finite Impulse Response (FIR) type has b
een
analysed employing NLMS (Normalized Least Mean Squa
re) algorithm.
An extensive study has been made
to investigate the effects of different parameters,
such as number of filter coefficients, number of s
amples,
step size, and input noise level, on the performanc
e of the adaptive noise cancelling system. All the
results
have been obtained using computer simulations built
on MATLAB platform.
Speech Analysis and synthesis using VocoderIJTET Journal
Abstract— In this paper, I proposed a speech analysis and synthesis using a vocoder. Voice conversion systems do not create new speech signals, but just transform existing one. The proposed speech vocoding is different from speech coding. To analyze the speech signal and represent it with less number of bits, so that bandwidth efficiency can be increased. The Synthesis of speech signal from the received bits of information. In this paper three aspects of analysis have been discussed: pitch refinement, spectral envelope estimation and maximum voiced frequency estimation. A Quasi-harmonic analysis model can be used to implement a pitch refinement algorithm which improves the accuracy of the spectral estimation. Harmonic plus noise model to reconstruct the speech signal from parameter. Finally to achieve the highest possible resynthesis quality using the lowest possible number of bits to transmit the speech signal. Future work aims at incorporating the phase information into the analysis and modeling process and also synthesis these three aspects in different pitch period.
Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size (EDNSS) algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original EDNSS algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real-world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
Speech Enhancement for Nonstationary Noise Environmentssipij
In this paper, we present a simultaneous detection and estimation approach for speech enhancement in nonstationary noise environments. A detector for speech presence in the short-time Fourier transform domain is combined with an estimator, which jointly minimizes a cost function that takes into account both detection and estimation errors. Under speech-presence, the cost is proportional to a quadratic spectral amplitude error, while under speech-absence, the distortion depends on a certain attenuation factor. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach which facilitate suppression of nonstationary noise with a controlled level of speech distortion.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
A new two stage FxNLMS algorithm based ANC system with secondary path modelling was proposed in
[15].Performance analysis of this system with real signals has been carried out using computer simulation.
Simulation studies showed that the system after trained with WGN can be successfully used for reducing
wide sense broad band noise.This paper also explores the intelligibility of speech signals in the quiet zone
created by the ANC system. The utterances of phonetically balanced Harvard sentences with different
SNRs are propagated through the quiet zone and the intelligibility of the resultant speech signal is
measured using MOS (Mean Opinion Score) test. Encouraging results are obtained which indicates that
the two stage FxNLMS algorithm based ANC system can find application in mobile phone communication.
Speech Recognition Systems(SRS) have been implemented by various processors including the digital signal processors(DSPs) and field programmable gate arrays(FPGAs) and their performance has been reported in literature. The fundamental purpose of speech is communication, i.e., the transmission of messages.In the case of speech, the fundamental analog form of the message is an acoustic waveform, which we call the speech signal. Speech signals can be converted to an electrical waveform by a microphone, further manipulated by both analog and digital signal processing, and then converted back to acoustic form by a loudspeaker, a telephone handset or headphone, as desired.The recognition of speech requires feature extraction and classification. The systems that use speech as input require a microcontroller to carry out the desired actions. In this paper, Cypress Programmable System on Chip (PSoC) has been studied and used for implementation of SRS. From all the available PSoCs, PSoC5 containing ARM Cortex-M3 as its CPU is used. The noise signals are firstly nullified from the speech signals using LogMMSE filtering. These signals are then sent to the PSoC5 wherein the speech is recognized and desired actions are performed.
Adaptive wavelet thresholding with robust hybrid features for text-independe...IJECEIAES
The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal background model Gaussian mixture model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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METHOD FOR REDUCING OF NOISE BY IMPROVING SIGNAL-TO-NOISE-RATIO IN WIRELESS LAN
1. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011
DOI : 10.5121/ijnsa.2011.3509 115
METHOD FOR REDUCING OF NOISE BY IMPROVING
SIGNAL-TO-NOISE-RATIO IN WIRELESS LAN
Dr.R.Seshadri 1
and Prof.N..Penchalaiah 2
1
Prof & Director of university computer center, S.V.University, Tirupati, India
ravalaseshadri@gmail.com
2
Department of Computer Science Engineering, ASCET, Gudur, India
pench_n@yahoo.com
ABSTRACT
The signal to noise ratio (SNR) is one of the important measures for reducing the noise.A technique that
uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise
reduction in a speech and image degraded by additive background noise is proposed. Since a speech
signal can be represented as the stationary signal over a short interval of time, most of speech signal can
be predicted by the LPEF. This estimation is performed by the ADF which is used as system
identification. Noise reduction is achieved by subtracting the reconstructed noise from the speech
degraded by additive background noise. Most of the MR image accelerating methods suffers from
degradation of acquired images, which is often correlated with the degree of acceleration. However,
Wideband MRI is a novel technique that transcends such flaws.In this paper we proposed LPEF and
ADF for reducing the noise in speech and also we demonstrate that Wideband MRI is capable of
obtaining images with identical quality as conventional MR images in terms of SNR in wireless LAN.
1. INTRODUCTION
In recent years, research on methods of noise reduction in a speech degraded by additive
background noise is actively being done by the use of microphone array [1], spectrum
subtraction (SS) [2], etc. Imperfection can be seen in the method of the noise reduction using
two microphones which can be considered as a directional microphone with a blind spot in the
arrival bearing of the noise. When many noise sources exist, an increase in number of
microphones cannot be avoided. It is therefore important to develop a noise reduction method
which uses a single microphone, and which can cancel multiple noise sources. In the systems
with only one microphone, extracting a speech &om a speech degraded by additive background
noise requires the use of SS method. One of the SS methods [2] improves the signal to noise
ratio(SNR) at the expense of processing delay, signal distortion and musical tones that arise due
to the residual noise. Moreover, SS method needs an advance estimation of noise spectrum. It
means that the SS method requires voicehoiceless section detector under the practical
environment. In order to improve on these negative effects, we have investigated a noise
redudon method based on linear prediction [3]. In this method, a noise reduction is efficiently
performed for additive white noise, because the coefficients of the linear predictor converge
such that the prediction error signal becomes white [4]. However, when a background noise is
colored, effectiveness of the noise reduction decreases since the linear predictor estimates both
a speech and a colored noise spectrum.
2. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011
116
It is known that the coefficients of the LPEF converge such that the prediction error signal
becomes white [4]. Since a speech signal can be represented as the stationary signal over a
short interval of time, most of speech signal is predicted by the LPEF. On the other hand, when
the input signal of' the LPEF is a background noise, the prediction error signal becomes white.
Assuming that the background noise is generated by exciting a linear system with a white noise,
then we can reconstruct the background noise from the prediction error signal by estimating the
transfer function of noise generation system. This estimation is performed by the ADF which is
used for system identification. Noise reduction is achieved by subtracting the reconstructed
noise from the speech degraded by additive background noise.
In this paper, we propose a technique that uses a linear prediction error filter (LPEF) and an
adaptive digital filter (ADF) to achieve noise reduction in a speech in wireless LAN.
The concept of Wideband MRI is based on the multi-carrier modulation technique in wireless
communications and it increases the bandwidth in MRI. Wideband MRI simultaneously excites
and acquires multiple slices using signals with multiple frequencies. RF pulses in Wideband
MRI contains several bands, we define the number of bands as “Wideband multi-slice/slab
factor W”. It is the excitation/acquisition of this wideband signal that provides additional
information necessary for acceleration. We have reported Wideband MRI acceleration
technique and its potential previously [1]. Several promising applications can be realized with
the help of this technique and we have demonstrated them successfully [2,3,4]. Nevertheless,
image properties of the Wideband MRI technique have to be thoroughly examined in order to
ensure its feasibility and validity. In recent years, several proposals of using wireless
transmission for MRI have been reported to avoid the interference between array channels [1-
3]. Amplitude modulation (AM) and single sideband (SSB) analog wireless techniques have
been applied to design transponders for RF coils [4].Compared to this analog transmission
technique, digital transmission has advantages of better noise immunity, more stability and
flexibility, and is code error free. In this work, we have designed and implemented a digital
transmission system based on WLAN 802.11b standard, which can reach the speed of 11Mbps
with 2.4G band.
2. THE PROPOSED NOISE REDUCTION METHOD
In this section, we describe the principle of the proposed noise reduction method using the
LPEF and the ADF. Below equation shows a transversal type LPEF, where x(n) is a speech
v(n) degraded by background noise e(n)= x(n)-y(n) is the prediction error signal, and An) is the
predicting signal, y(n) is given by
where hk(n) (k1,2, ..., M) are the tap coefficients [4]. The coefficients of the LPEF will
converge such that the prediction error signal e(n) becomes white. When small step size is used
in the algorithm for updating the coefficients of the LPEF, the LPEF estimates the input signal
3. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011
117
with high fidelity at the expense of poor tracking ability. On the other hand, when the large step
size is used in the algorithm, the LPEF estimates the input signal with high tracking ability at
the expense of roughly estimating. Therefore, it is required to whiten the input signal x(n) that
the large step size is used in the algorithm for whitening the non-stationary speech signal, and
the small step size is used in the algorithm for whitening the noise signal. Specifically, the
algorithm with large step size is used for updating the LPEF’s coefficients corresponding to the
samples around the sample delayed by a pitch period. The other coefficients of the LPEF are
updated by using the algorithm with small step size. Then the LPEF estimates the voiced
speech with high tracking ability, and estimates the other signal with high fidelity. Thereby, the
signal whitening by the LPEF is efficiently performed. Next, we consider the reconstruction of
the background noise from the prediction error signal.
Assuming that the background noise is generated by exciting a linear system with a white noise
as shown in Fig. 2, then we can consider the system identification model as shown in Fig. 3(a)
and (b), where H,(z) represents the transfer function of noise generation linear system,
HADF(z) is the transfer function of the ADF, hi(n),h,’(n), ..., hi(.) are the tap coefficients of the
ADF,&n is the reconstructed noise, and ;(n) is the extracted speech. The ADF
cannot estimate the v(n) due to the e(n) which does not correlated with the voiced speech v(n).
Then the output of the ADF represents only the background noise. However, when the
unvoiced speech whose source is represented as random noise is observed in the v(n), a
probability of reconstructing the unvoiced speech by the ADF arises. In order to improve this
problem, we use the long time average of the error signal e,”() for updating the coefficients of
the ADF since that the unvoiced speech vanishes in a short time in comparison to the noise.
Thereby, the probability of
Figure 1. LPEF (Linear Prediction Error Fileter)
Figure 2. Noise generation system
4. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011
118
(a) Block diagram of System identification model
(b) Structure of ADF (Adaptive Digital Filter)
Figure 3. System Identification Model
Reconstructing the unvoiced speech will be decreased. That is, we apply the small step size to
the adaptive algorithm for updating the coefficients of the ADF. We shall now incorporate the
signal whitening Process with the noise reconstruction process for noise reduction. The
proposed noise reduction system is shown in Fig. 4, where the &.EF(z) represents the transfer
function of the LPEF. The noise reduction is achieved by subtracting the reconstructed noise ( n
) from the input signal x(n).
3. MATERIALS AND METHODS
To compare the image quality with and without wideband MRI, two identical standard Broker
head phantoms were used. Each phantom has several structures for image quality analysis.
There are 1.4mm and 1mm wide horizontal and vertical stripes specifically tailored for image
resolution assessment while some cylindrical structures containing oil and porous materials can
be used for contrast evaluation. All images were acquired on a 3T Brokers Biopsies MR
imaging system, with the use of 8826 head coil. The two phantoms were placed 14cm apart
along the axial direction in the RF coil. Axial images of the two phantoms were first taken
separately without using the Wideband MRI technique. Then a Wideband factor of W=2 was
applied by a modified Sinc RF pulse to obtain simultaneously the images of both phantoms
using half the total scan time. Imaging parameters are listed as below: FOV=19cmx19cm, total
Matrix size=256x256, gradient echo sequence with TR/TE=30/6.3ms. To analyze the image,
SNR signal was sampled from 9 uniform areas throughout the image and noise was calculated
as the standard deviation at four corners of the image.
5. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.5, Sep 2011
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The complete images need both real and imagery parts. However, imaginary parts of imaging
signals can not be generated just through some simple algorithms after AD conversion. Because
it is difficult to decide the sampling rate of sine wave and cosine wave in one cycle to make real
and imaginary signals synchronous. The synchronous imaginary part of MR imaging signals
could be completed in hardware for the real part circuit simultaneously.
4. CONCLUSION
We have proposed a new noise reduction method using linear prediction error filter and
adaptive digital filter. From the experimental results, it was observed that there was
improvement of SNR in the extracted voice signal, and this proposed noise reduction method is
also available under the practical environment. Further researches involve an improvement of
the tracking ability to the non-stationary noise like a tunnel noise, a reduction of the residual
noise and a performance evaluation in a test product. We believe it is self-evident that
Wideband MRI is a powerful MRI accelerating technique that can maintain the same image
quality of each accelerated image while other acceleration methods suffer from degradation
such as SNR loss or artifacts. Since Wideband MRI accelerates by the increase in bandwidth
instead of k-space or image space alteration, properties stated in this study can be further
extended into higher Wideband accelerated images.
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Authors:
Prof.N.Penchalaiah Research Scholar in SV University, Tirupati and
Working as Professor in CSE Dept,ASCET,Gudur. He was completed his
M.Tech in Sathyabama University in 2006. He has 11 years of teaching
experience. He guided PG & UG Projects. He published 2 National
Conferences and 6 Inter National Journals.
Dr.R.Seshadri Working as Professor & Director, University Computer
Centre, Sri Venkateswara University, Tirupati. He was completed his PhD
in S.V.University in 1998 in the field of “ Simulation Modeling &
Compression of E.C.G. Data Signals (Data compression Techniques)
Electronics & Communication Engg.”. He has richest of knowledge in
Research field, he is guiding 10 Ph.D in Fulltime as well as Part time. He
has vast experience in teaching of 26 years. He published 10 national and
international conferences and 15 papers published different Journals.