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 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
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.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
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.
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Multistage Implementation of Narrowband LPF by Decimator in Multirate DSP App...iosrjce
Decimator is an important sampling device used for multi-rate signal processing in wireless
communication systems. Multirate systems have traditionally played the important role in compression for
contemporary communication application.In this paper it demonstrated that a multistage implementation of
sampling rate conversion often provides for a more efficient realization, especially when filter specifications are
very tight (e.g., a narrow pass band and narrow transition band) and there are a audio-band of 4kHz bandwidth
and compression by decimator to isolate the frequency component 80Hz.The LPF used by the decimator are
acquire by two different approaches: first is the window method and other is frequency sampling
method,Multistages implementations are used to further reduce the computational load. This approach
drastically reduces the filter order and also reduces computational cost. Here it have reduce the overall
computational complexity at single stage is 50 times and for second stages is near about 9 times reduce by the
decimator factor is 50.
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
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.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
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.
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Multistage Implementation of Narrowband LPF by Decimator in Multirate DSP App...iosrjce
Decimator is an important sampling device used for multi-rate signal processing in wireless
communication systems. Multirate systems have traditionally played the important role in compression for
contemporary communication application.In this paper it demonstrated that a multistage implementation of
sampling rate conversion often provides for a more efficient realization, especially when filter specifications are
very tight (e.g., a narrow pass band and narrow transition band) and there are a audio-band of 4kHz bandwidth
and compression by decimator to isolate the frequency component 80Hz.The LPF used by the decimator are
acquire by two different approaches: first is the window method and other is frequency sampling
method,Multistages implementations are used to further reduce the computational load. This approach
drastically reduces the filter order and also reduces computational cost. Here it have reduce the overall
computational complexity at single stage is 50 times and for second stages is near about 9 times reduce by the
decimator factor is 50.
The Short-Time Silence of Speech Signal as Signal-To-Noise Ratio EstimatorIJERA Editor
It is proposed in this paper to use a small portion of the audio speech signal to estimate Signal-to-Noise Ratio
(SNR). It is found that, the first 30 ms duration has enough information about the SNR in advance. The first 30
ms of a recorded speech usually comes from the silence rather than speech. This is because the speaker usually
starts the recording process or wait for it before he/she can deliver the utterance. For testing and comparing the
proposed estimator, different noisy corpora are built upon the TIMIT data. The average estimation of the
suggested algorithm proves to get better results as compared to the Waveform Amplitude Distribution Analysis
(WADA) and the National Institute of Standard and Technology (NIST) SNR estimators. The complexity of the
STS-SNR estimator is less than both as it only processes a small portion of the audio samples
Dwpt Based FFT and Its Application to SNR Estimation in OFDM SystemsCSCJournals
In this paper, wavelet packet (WP) based FFT and its application to SNR estimation is proposed. OFDM systems demodulate data using FFT. The proposed solution computes the exact FFT using WP and its computational complexity is of the same order as FFT, i.e. O (Nlog2 N). SNR estimation is done inside wavelet packet based FFT block unlike previous SNR estimations techniques which perform SNR estimation after FFT. Wavelet packet analyzed data is used to perform SNR estimation in colored noise. The proposed estimator takes into consideration the different noise power levels of the colored noise over the OFDM sub-carriers. The OFDM band is divided into several sub-bands using wavelet packet and noise in each sub-band is considered white. The second-order statistics of the transmitted OFDM preamble are calculated in each sub-band and the power noise is estimated. The proposed estimator is compared with Reddy’s estimator for colored noise in terms of mean squared error (MSE).
Broad phoneme classification using signal based featuresijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.
Audio/Speech Signal Analysis for Depressionijsrd.com
The word “depressed†is a common everyday word. People might say "I am depressed" when in fact they mean "I am fed up because I have had a row, or failed an exam, or lost my job", etc. These ups and downs of life are common and normal. Most people recover quite quickly. Depression is identified by different methods. Here we are identified depression by MFCC (Mel Frequency Ceptral Coefficient) method. There are different parameters used for the identification of depressed speech and normal speech, but MFCCs based parameter is the most applicable information then other parameter because depressive speech or audio signal can contain more information in the higher energy bands when compared with normal speech.
Frequency based criterion for distinguishing tonal and noisy spectral componentsCSCJournals
A frequency-based criterion for distinguishing tonal and noisy spectral components is proposed. For considered spectral local maximum two instantaneous frequency estimates are determined and the difference between them is used in order to verify whether component is noisy or tonal. Since one of the estimators was invented specially for this application its properties are deeply examined. The proposed criterion is applied to the stationary and nonstationary sinusoids in order to examine its efficiency.
Comparative performance analysis of channel normalization techniqueseSAT Journals
Abstract A major part of the interaction between humans takes place via speech communication. The speech signal carries both useful and unwanted information. Processing of such signals involve enhancing the useful information. The intelligibility of speech signals is significantly reduced due to the presence of unwanted information such as noise. Channel normalization algorithms suppress such additive noise introduced in the speech signals by transmission channel or by recording environment conditions. Enhancing the quality and intelligibility of speech signals improve the performance of speech systems such as Automatic speech recognition (ASR) , voice communication and hearing aids to name the few. Based on the experimental results the comparative analysis of channel normalization techniques have been presented in this paper to find out the most suitable algorithm for enhancing the speech signals. Keywords: Cepstral Mean Normalization, Spectral Subtraction, Weiner filter, Signal to Noise Ratio
Reduced Ordering Based Approach to Impulsive Noise Suppression in Color ImagesIDES Editor
In this paper a novel filtering design intended for
the impulsive noise removal in color images is presented.
The described scheme utilizes the rank weighted cumulated
distances between the pixels belonging to the local filtering
window. The impulse detection scheme is based on the
difference between the aggregated weighted distances assigned
to the central pixel of the window and the minimum value,
which corresponds to the rank weighted vector median. If the
difference exceeds an adaptively determined threshold value,
then the processed pixel is replaced by the mean of the
neighboring pixels, which were found to be not corrupted,
otherwise it is retained. The important feature of the described
filtering framework is its ability to effectively suppress
impulsive noise, while preserving fine image details. The
comparison with the state-of-the-art denoising schemes
revealed that the proposed filter yields better restoration
results in terms of objective restoration quality measures.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In most of the communication systems speech is transmittes in narrowband, containing frequencies from 300 Hz to 3400 Hz. Compared with normal speech which is generally contains a perceptually significant amount of energy up to 8 kHz, this speech has a muffled quality and reduced intelligibility, particularly noticeable in sounds such as /s/ and /f/ . Speech which has been bandlimited to 8 kHz is often coded for this reason, but this requires an increase in the bit rate.
Wideband reconstruction is a scheme that adds a synthesized highband signal to narrowband speech to produce a higher quality wideband speech signal. The synthesized highband signal is based entirely on information contained in the narrowband speech, and is thus achieved at zero increase in the bit rate from a coding perspective. Wideband reconstruction can function as a post-processor to any narrowband telephone receiver, or alternatively it can be combined with any narrowband speech coder to produce a very low bit rate wideband speech coder. Applications include higher quality mobile, teleconferencing, and internet telephony.
This final project aims to simulate the bandwidth extension system using spectral shifting method for highband excitation, which is used codebook and linear mapping to estimate the envelope of highband. The algorithm for wide band expansion proved to work, though certain unwanted artefacts were introduced in the reconstructed signal. Listening tests confirmed the presence of these unwanted artefacts. Objective and subjective tests demonstrate that wideband speech synthesized using these techniques have presentage in (numerical) 50 % of the respondences with SNR 5,13 dB. Optimum parameter used in this system goes to Euclidean distance with K=1 for KNN classification and correlation distance with 256 clusters for Kmean clustering. Computational time for spectral shifting 0.144 s, for spectral folding 0.138 s and codebook needs 164,2 s. Subjective measurement using DMOS for spectral shifting about 3.65 and for spectral folding 2. However further research and improvement to reach higher quality from this system for implementation are still needed.
A New Speech Enhancement Technique to Reduce Residual Noise Using Perceptual ...IOSR Journals
This paper deals with residual musical noise which results from the perceptual speech enhancement
type algorithms and especially using wiener filtering approach. Perceptual speech enhancement techniques
perform better than the non perceptual techniques, most of them still return a trouble residual musical noise.
This is due to that only noise above the noise masking threshold (NMT) is filtered out then noise below the noise
masking threshold (NMT) can become audible if its maskers are filtered. It can affect the performance of
perceptual speech enhancement method that process the audible noise only (Residual noise is still present). In
order to overcome this drawback a new speech enhancement technique is proposed here.The main aim here is to improve the enhanced speech signal quality provided by perceptual wiener filtering and by controlling the
latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results
gives the information that the performance is improved compared to other perceptual speech enhancement
methods
Improving the Efficiency of Spectral Subtraction Method by Combining it with ...IJORCS
In the field of speech signal processing, Spectral subtraction method (SSM) has been successfully implemented to suppress the noise that is added acoustically. SSM does reduce the noise at satisfactory level but musical noise is a major drawback of this method. To implement spectral subtraction method, transformation of speech signal from time domain to frequency domain is required. On the other hand, Wavelet transform displays another aspect of speech signal. In this paper we have applied a new approach in which SSM is cascaded with wavelet thresholding technique (WTT) for improving the quality of speech signal by removing the problem of musical noise to a great extent. Results of this proposed system have been simulated on MATLAB.
METHOD FOR REDUCING OF NOISE BY IMPROVING SIGNAL-TO-NOISE-RATIO IN WIRELESS LANIJNSA Journal
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.
Compressive speech enhancement using semi-soft thresholding and improved thre...IJECEIAES
Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basisfunction based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silenceregion of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The Short-Time Silence of Speech Signal as Signal-To-Noise Ratio EstimatorIJERA Editor
It is proposed in this paper to use a small portion of the audio speech signal to estimate Signal-to-Noise Ratio
(SNR). It is found that, the first 30 ms duration has enough information about the SNR in advance. The first 30
ms of a recorded speech usually comes from the silence rather than speech. This is because the speaker usually
starts the recording process or wait for it before he/she can deliver the utterance. For testing and comparing the
proposed estimator, different noisy corpora are built upon the TIMIT data. The average estimation of the
suggested algorithm proves to get better results as compared to the Waveform Amplitude Distribution Analysis
(WADA) and the National Institute of Standard and Technology (NIST) SNR estimators. The complexity of the
STS-SNR estimator is less than both as it only processes a small portion of the audio samples
Dwpt Based FFT and Its Application to SNR Estimation in OFDM SystemsCSCJournals
In this paper, wavelet packet (WP) based FFT and its application to SNR estimation is proposed. OFDM systems demodulate data using FFT. The proposed solution computes the exact FFT using WP and its computational complexity is of the same order as FFT, i.e. O (Nlog2 N). SNR estimation is done inside wavelet packet based FFT block unlike previous SNR estimations techniques which perform SNR estimation after FFT. Wavelet packet analyzed data is used to perform SNR estimation in colored noise. The proposed estimator takes into consideration the different noise power levels of the colored noise over the OFDM sub-carriers. The OFDM band is divided into several sub-bands using wavelet packet and noise in each sub-band is considered white. The second-order statistics of the transmitted OFDM preamble are calculated in each sub-band and the power noise is estimated. The proposed estimator is compared with Reddy’s estimator for colored noise in terms of mean squared error (MSE).
Broad phoneme classification using signal based featuresijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.
Audio/Speech Signal Analysis for Depressionijsrd.com
The word “depressed†is a common everyday word. People might say "I am depressed" when in fact they mean "I am fed up because I have had a row, or failed an exam, or lost my job", etc. These ups and downs of life are common and normal. Most people recover quite quickly. Depression is identified by different methods. Here we are identified depression by MFCC (Mel Frequency Ceptral Coefficient) method. There are different parameters used for the identification of depressed speech and normal speech, but MFCCs based parameter is the most applicable information then other parameter because depressive speech or audio signal can contain more information in the higher energy bands when compared with normal speech.
Frequency based criterion for distinguishing tonal and noisy spectral componentsCSCJournals
A frequency-based criterion for distinguishing tonal and noisy spectral components is proposed. For considered spectral local maximum two instantaneous frequency estimates are determined and the difference between them is used in order to verify whether component is noisy or tonal. Since one of the estimators was invented specially for this application its properties are deeply examined. The proposed criterion is applied to the stationary and nonstationary sinusoids in order to examine its efficiency.
Comparative performance analysis of channel normalization techniqueseSAT Journals
Abstract A major part of the interaction between humans takes place via speech communication. The speech signal carries both useful and unwanted information. Processing of such signals involve enhancing the useful information. The intelligibility of speech signals is significantly reduced due to the presence of unwanted information such as noise. Channel normalization algorithms suppress such additive noise introduced in the speech signals by transmission channel or by recording environment conditions. Enhancing the quality and intelligibility of speech signals improve the performance of speech systems such as Automatic speech recognition (ASR) , voice communication and hearing aids to name the few. Based on the experimental results the comparative analysis of channel normalization techniques have been presented in this paper to find out the most suitable algorithm for enhancing the speech signals. Keywords: Cepstral Mean Normalization, Spectral Subtraction, Weiner filter, Signal to Noise Ratio
Reduced Ordering Based Approach to Impulsive Noise Suppression in Color ImagesIDES Editor
In this paper a novel filtering design intended for
the impulsive noise removal in color images is presented.
The described scheme utilizes the rank weighted cumulated
distances between the pixels belonging to the local filtering
window. The impulse detection scheme is based on the
difference between the aggregated weighted distances assigned
to the central pixel of the window and the minimum value,
which corresponds to the rank weighted vector median. If the
difference exceeds an adaptively determined threshold value,
then the processed pixel is replaced by the mean of the
neighboring pixels, which were found to be not corrupted,
otherwise it is retained. The important feature of the described
filtering framework is its ability to effectively suppress
impulsive noise, while preserving fine image details. The
comparison with the state-of-the-art denoising schemes
revealed that the proposed filter yields better restoration
results in terms of objective restoration quality measures.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In most of the communication systems speech is transmittes in narrowband, containing frequencies from 300 Hz to 3400 Hz. Compared with normal speech which is generally contains a perceptually significant amount of energy up to 8 kHz, this speech has a muffled quality and reduced intelligibility, particularly noticeable in sounds such as /s/ and /f/ . Speech which has been bandlimited to 8 kHz is often coded for this reason, but this requires an increase in the bit rate.
Wideband reconstruction is a scheme that adds a synthesized highband signal to narrowband speech to produce a higher quality wideband speech signal. The synthesized highband signal is based entirely on information contained in the narrowband speech, and is thus achieved at zero increase in the bit rate from a coding perspective. Wideband reconstruction can function as a post-processor to any narrowband telephone receiver, or alternatively it can be combined with any narrowband speech coder to produce a very low bit rate wideband speech coder. Applications include higher quality mobile, teleconferencing, and internet telephony.
This final project aims to simulate the bandwidth extension system using spectral shifting method for highband excitation, which is used codebook and linear mapping to estimate the envelope of highband. The algorithm for wide band expansion proved to work, though certain unwanted artefacts were introduced in the reconstructed signal. Listening tests confirmed the presence of these unwanted artefacts. Objective and subjective tests demonstrate that wideband speech synthesized using these techniques have presentage in (numerical) 50 % of the respondences with SNR 5,13 dB. Optimum parameter used in this system goes to Euclidean distance with K=1 for KNN classification and correlation distance with 256 clusters for Kmean clustering. Computational time for spectral shifting 0.144 s, for spectral folding 0.138 s and codebook needs 164,2 s. Subjective measurement using DMOS for spectral shifting about 3.65 and for spectral folding 2. However further research and improvement to reach higher quality from this system for implementation are still needed.
A New Speech Enhancement Technique to Reduce Residual Noise Using Perceptual ...IOSR Journals
This paper deals with residual musical noise which results from the perceptual speech enhancement
type algorithms and especially using wiener filtering approach. Perceptual speech enhancement techniques
perform better than the non perceptual techniques, most of them still return a trouble residual musical noise.
This is due to that only noise above the noise masking threshold (NMT) is filtered out then noise below the noise
masking threshold (NMT) can become audible if its maskers are filtered. It can affect the performance of
perceptual speech enhancement method that process the audible noise only (Residual noise is still present). In
order to overcome this drawback a new speech enhancement technique is proposed here.The main aim here is to improve the enhanced speech signal quality provided by perceptual wiener filtering and by controlling the
latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results
gives the information that the performance is improved compared to other perceptual speech enhancement
methods
Improving the Efficiency of Spectral Subtraction Method by Combining it with ...IJORCS
In the field of speech signal processing, Spectral subtraction method (SSM) has been successfully implemented to suppress the noise that is added acoustically. SSM does reduce the noise at satisfactory level but musical noise is a major drawback of this method. To implement spectral subtraction method, transformation of speech signal from time domain to frequency domain is required. On the other hand, Wavelet transform displays another aspect of speech signal. In this paper we have applied a new approach in which SSM is cascaded with wavelet thresholding technique (WTT) for improving the quality of speech signal by removing the problem of musical noise to a great extent. Results of this proposed system have been simulated on MATLAB.
METHOD FOR REDUCING OF NOISE BY IMPROVING SIGNAL-TO-NOISE-RATIO IN WIRELESS LANIJNSA Journal
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.
Compressive speech enhancement using semi-soft thresholding and improved thre...IJECEIAES
Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basisfunction based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silenceregion of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.
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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.
Teager Energy Operation on Wavelet Packet Coefficients for Enhancing Noisy Sp...CSCJournals
In this paper a new thresholding based speech enhancement approach is presented, where the threshold is statistically determined by employing the Teager energy operation on the Wavelet Packet (WP) coefficients of noisy speech. The threshold thus obtained is applied on the WP coefficients of the noisy speech by using a hard thresholding function in order to obtain an enhanced speech. Detailed simulations are carried out in the presence of white, car, pink, and babble noises to evaluate the performance of the proposed method. Standard objective measures, spectrogram representations and subjective listening tests show that the proposed method outperforms the existing state-of-the-art thresholding based speech enhancement approaches for noisy speech from high to low levels of SNR.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
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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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Speech Enhancement for Nonstationary Noise Environmentssipij
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Compressive Sensing in Speech from LPC using Gradient Projection for Sparse R...IJERA Editor
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speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily.
This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete
points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient
Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The
experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
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to improve the enhanced speech signal quality provided by perceptual wiener filtering and by controlling the
latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results
gives the information that the performance is improved compared to other perceptual speech enhancement
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Improvement of minimum tracking in Minimum Statistics noise estimation method
1. Hassan Farsi
Signal Processing: An International Journal (SPIJ), Volume (4); Issue (1) 17
Improvement of Minimum Tracking in Minimum Statistics Noise
Estimation Method
Hassan Farsi hfarsi@birjand.ac.ir
Department of Electronics and Communications Engineering,
University of Birjand,
Birjand, IRAN.
Abstract
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 non-
stationary 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.
Keywords: Speech enhancement, Statistics noise, noise cancellation, Short time Fourier transform
1. INTRODUCTION
Noise spectrum estimation is a fundamental component of speech enhancement and speech
recognition systems. The robustness of such systems, particularly under low signal-to-noise ratio
(SNR) conditions and non-stationary noise environments, is greatly affected by the capability to
reliably track fast variations in the statistics of the noise. Traditional noise estimation methods, which
are based on voice activity detectors (VAD's), restrict the update of the estimate to periods of speech
absence.
Additionally, VAD's are generally difficult to tune and their reliability severely deteriorates for weak
speech components and low input SNR [1], [2], [3]. Alternative techniques, based on histograms in
the power spectral domain [4], [5], [6], are computationally expensive, require much memory
resources, and do not perform well in low SNR conditions. Furthermore, the signal segments used for
building the histograms are typically of several hundred milliseconds, and thus the update rate of the
noise estimate is essentially moderate.
Martin (2001)[7] proposed a method for estimating the noise spectrum based on tracking the
minimum of the noisy speech over a finite window. As the minimum is typically smaller than the mean,
unbiased estimates of noise spectrum were computed by introducing a bias factor based on the
statistics of the minimum estimates. The main drawback of this method is that it takes slightly more
than the duration of the minimum-search window to update the noise spectrum when the noise floor
increases abruptly. Moreover, this method may occasionally attenuate low energy phonemes,
particularly if the minimum search window is too short [8]. These limitations can be overcome, at the
price of significantly higher complexity, by adapting the smoothing parameter and the bias
compensation factor in time and frequency [9]. A computationally more efficient minimum tracking
scheme is presented in [10]. Its main drawbacks are the very slow update rate of the noise estimate in
case of a sudden rise in the noise energy level, and its tendency to cancel the signal [1].In this paper
we propose a new approach for minimum tracking , resulted improving the performance of MS
method.
The paper is organized as follows. In Section II, we present the MS noise estimator. In Section III, we
introduce an method for minimum tracking, and in section IV, evaluate the proposed method, and
discuss experimental results, which validate its effectiveness.
2. MINIMUM STATISTICS NOISE ESTIMATOR
Let x(n) and d(n) denote speech and uncorrelated additive noise signals, respectively, where n is a
discrete-time index. The observed signal y(n), given by y(n)=x(n)+d(n), is divided into overlapping
frames by the application of a window function and analyzed using the short-time Fourier transform
(STFT). Specifically,
(1)
2. Hassan Farsi
Signal Processing: An International Journal (SPIJ), Volume (4); Issue (1) 18
Where k is the frequency bin index, is the time frame index, h is an analysis window of size N (e.g.,
Hamming window), and M is the framing step (number of samples separating two successive frames).
Let and denote the STFT of the clean speech and noise, respectively.
For noise estimation in MS method, first compute the short time subband signal power using
recursively smoothed periodograms. The update recursion is given by eq.(2). The smoothing constant
is typically set to values between .
(2)
The noise power estimate is obtained as a weighted minimum of the short time power
estimate within window of D subband power samples [11], i.e.
(3)
is the estimated minimum power and is a factor to compensate the bias of the
minimum estimate. The bias compensation factor depends only on known algorithmic parameters [7].
For reasons of computational complexity and delay the data window of length D is decomposed into U
sub-windows of length V such that For a sampling rate of fs=8 kHz and a framing step M=64 typical
window parameters are V=25 and U=4,thus D=100 corresponding to a time window of ((D-
1).M+N)/fs=0.824s. Whenever V samples are read, the minimum of the current sub-window is
determined and stored for later use. The overall minimum is obtained as the minimum of past
samples within the current sub-window and the U previous sub-window minima.
In [7] shown that the bias of the minimum subband power estimate is proportional to the noise power
and that the bias can be compensated by multiplying the minimum estimate with the inverse of
the mean computed for .
(4)
Therefore to obtain We must generate data of variance , compute the smoothed
periodogram (eq. (2)), and evaluate the mean and the variance of the minimum estimate.
As discussed earlier, minimum of the smoothed periodograms, obtained within window of D subband
power samples. In next section we propose a method to improve this minimum tracking.
3. PROPOSED METHOD FOR MINIMUM TRACKING
The local minimum in MS method was found by tracking the minimum of noisy speech over a search
window spanning D frames. Therefore, the noise update was dependent on the length of the
minimum-search window. The update of minimum can take at most 2D frames for increasing noise
levels. A different non-linear rule is used in our method for tracking the minimum of the noisy speech
by continuously averaging past spectral values [12]
(5)
3. Hassan Farsi
Signal Processing: An International Journal (SPIJ), Volume (4); Issue (1) 19
where is the local minimum of the noisy speech power spectrum and and are
constants which are determined experimentally. The lookahead factor controls the adaptation time
of the local minimum. Typically, we use , , and . Because Improve
the minimum tracking in this method, the bias compensation factor decreases, as in MS method it is
obtained and in this method it is obtained .
4. PERFORMANCE EVALUATION
The performance evaluation of the proposed method (PM), and a comparison to the MS method,
consists of three parts. First, we test the tracking capability of the noise estimators for non-stationary
noise. Second, we measure the segmental relative estimation error for various noise types and levels.
Third, we integrate the noise estimators into a speech enhancement system, and determine the
improvement in the segmental SNR. The results are conformed by a subjective study of speech
spectrograms and informal listening tests.
The noise signals used in our evaluation are taken from the Noisex92 database [13]. They include
white Gaussian noise (WGN), F16 cockpit noise, and babble noise. The speech signal is sampled at 8
kHz and degraded by the various noise types with segmental SNR's in the range [-5, 10] dB. The
segmental SNR is defined by [14]
(6)
where represents the set of frames that contain speech,
and its cardinality. The spectral analysis is implemented with Hamming windows of 256 samples
length (32ms) and 64 samples frame update step.
Fig. 1 plots the ideal (True), PM, and MS noise estimates for a babble noise at 0 dB segmental SNR,
and a single frequency bin k = 5 (the ideal noise estimate is taken as the recursively smoothed
periodogram of the noise , with a smoothing parameter set to 0.95). Clearly, the PM noise
estimate follows the noise power more closely than the MS noise estimate. The update rate of the MS
noise estimate is inherently restricted by the size of the minimum search window (D). By contrast, the
PM noise estimate is continuously updated even during speech activity.
Fig. 2 shows another example of the improved tracking capability of the PM estimator. In this case,
the speech signal is degraded by babble noise at 5 dB segmental SNR. The ideal, PM, and MS noise
estimates, averaged out over the frequency, are depicted in this figure.
A quantitative comparison between the PM and MS estimation methods is obtained by evaluating the
segmental relative estimation error in various environmental conditions. The segmental relative
estimation error is defined by [15]
(7)
where is the ideal noise estimate, is the noise estimated by the tested method, and L
is the number of frames in the analyzed signal. Table 1 presents the results of the segmental relative
estimation error achieved by the PM and MS estimators for various noise types and levels. It shows
that the PM method obtains significantly lower estimation error than the MS method.
The segmental relative estimation error is a measure that weighs all frames in a uniform manner,
without a distinction between speech presence and absence. In practice, the estimation error is more
consequential in frames that contain speech, particularly weak speech components, than in frames
that contain only noise. We therefore examine the performance of our estimation method when
integrated into a speech enhancement system. Specifically, the PM and MS noise estimators are
combined with the Optimally-Modified Log-Spectral Amplitude (OM-LSA) estimator, and evaluated
both objectively using an improvement in segmental SNR measure, and subjectively by informal
listening tests. The OM-LSA estimator [16], [17] is a modified version of the conventional LSA
estimator [18-19], based on a binary hypothesis model. The modification includes a lower bound for
the gain, which is determined by a subjective criterion for the noise naturalness, and exponential
weights, which are given by the conditional speech presence probability [20, 21].
4. Hassan Farsi
Signal Processing: An International Journal (SPIJ), Volume (4); Issue (1) 20
0 100 200 300 400 500 600 700 800 900 1000
60
65
70
75
Frame
(dB)
True noise
MS method
proposed method
FIGURE 1. Plot of true noise spectrum and estimated noise spectrum using proposed method and MS method
for a noisy speech signal degraded by babble noise at 0 dB segmental SNR, and a single frequency bin k = 5.
0 100 200 300 400 500 600 700 800 900 1000
53
54
55
56
57
58
59
60
61
62
63
Frame
(dB)
True noise
MS method
proposed method
FIGURE 2. Ideal, proposed and MS average noise estimates for babble noise at 5 dB segmental SNR.
5. Hassan Farsi
Signal Processing: An International Journal (SPIJ), Volume (4); Issue (1) 21
Babble Noise
MS PM
F16 Noise
MS PM
WGN Noise
MS PM
Input
SegSNR
(dB)
0.401 0.397
0.398 0.395
0.427 0.422
0.743 0.736
0.192 0.189
0.197 0.193
0.231 0.228
0.519 0.512
0.147 0.139
0.170 0.163
0.181 0.173
0.241 0.231
-5
0
5
10
TABLE 1. Segmental Relative Estimation Error for Various Noise Types and Levels, Obtained Using the MS and
proposed method (PM) Estimators.
Babble Noise
MS PM
F16 Noise
MS PM
WGN Noise
MS PM
Input
SegSNR
(dB)
3.254 3.310
2.581 2.612
2.648 2.697
1.943 1.998
6.879 6.924
6.025 6.165
5.214 5.298
3.964 4.034
8.213 8.285
7.231 7.312
6.215 6.279
5.114 5.216
-5
0
5
10
TABLE 2. Segmental SNR Improvement for Various Noise Types and Levels, Obtained Using the MS and
proposed method (PM) Estimators.
Table 2 summarizes the results of the segmental SNR improvement for various noise types and
levels. The PM estimator consistently yields a higher improvement in the segmental SNR, than the
MS estimator, under all tested environmental conditions.
5. SUMMARY AND CONCLUSION
In this paper we have addressed the issue of noise estimation for enhancement of noisy speech. The
noise estimate was updated continuously in every frame using minimum of the smoothed noisy
speech spectrum. Unlike the MS method, the update of local minimum was continuous over time and
did not depend on some fixed window length. Hence the update of noise estimate was faster for very
rapidly varying non-stationary noise environments. This was confirmed by formal listening tests that
indicated significantly higher preference for our proposed algorithm compared to the MS noise
estimation algorithm.
6. REFERENCES
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