Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Tr...Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.展开更多
The acoustic characteristics or the chinese vowels of 24 children with cleft palate and 10 normal control children were analyzed by computerized speech signal processing system (CSSPS),and the speech articulation was ...The acoustic characteristics or the chinese vowels of 24 children with cleft palate and 10 normal control children were analyzed by computerized speech signal processing system (CSSPS),and the speech articulation was judged with Glossary of clert palate speech(GCPS).The listening judgement showed that the speech articulation was significantly different between the two groups(P<0.01).The objective quantitative measurement suggested that the formant pattern(FP)of vowels in children with cleft palate was different from that of normal control children except vowel[a](P< 0.05).The acoustic vowelgraph or the Chinese vowels which demonstrated directly the relationship of vocal space and speech perception was stated with the first formant frequence(F1)and the second formant frequence(F2).The authors conclude that the values or F1 and F2 point out the upward and backward tongue movement to close the clert, which reflects the vocal characteristics of trausmission of clert palate speech.展开更多
This paper proposes a multi-band speech enhancement algorithm exploiting iterative processing for enhancement of single channel speech. In the proposed algorithm, the output of the multi-band spectral subtraction (MBS...This paper proposes a multi-band speech enhancement algorithm exploiting iterative processing for enhancement of single channel speech. In the proposed algorithm, the output of the multi-band spectral subtraction (MBSS) algorithm is used as the input signal again for next iteration process. As after the first MBSS processing step, the additive noise transforms to the remnant noise, the remnant noise needs to be further re-estimated. The proposed algorithm reduces the remnant musical noise further by iterating the enhanced output signal to the input again and performing the operation repeatedly. The newly estimated remnant noise is further used to process the next MBSS step. This procedure is iterated a small number of times. The proposed algorithm estimates noise in each iteration and spectral over-subtraction is executed independently in each band. The experiments are conducted for various types of noises. The performance of the proposed enhancement algorithm is evaluated for various types of noises at different level of SNRs using, 1) objective quality measures: signal-to-noise ratio (SNR), segmental SNR, perceptual evaluation of speech quality (PESQ);and 2) subjective quality measure: mean opinion score (MOS). The results of proposed enhancement algorithm are compared with the popular MBSS algorithm. Experimental results as well as the objective and subjective quality measurement test results confirm that the enhanced speech obtained from the proposed algorithm is more pleasant to listeners than speech enhanced by classical MBSS algorithm.展开更多
Sound indexing and segmentation of digital documentsespecially in the internet and digital libraries are very useful tosimplify and to accelerate the multimedia document retrieval. Wecan imagine that we can extract mu...Sound indexing and segmentation of digital documentsespecially in the internet and digital libraries are very useful tosimplify and to accelerate the multimedia document retrieval. Wecan imagine that we can extract multimedia files not only bykeywords but also by speech semantic contents. The maindifficulty of this operation is the parameterization and modellingof the sound track and the discrimination of the speech, musicand noise segments. In this paper, we will present aSpeech/Music/Noise indexing interface designed for audiodiscrimination in multimedia documents. The program uses astatistical method based on ANN and HMM classifiers. After preemphasisand segmentation, the audio segments are analysed bythe cepstral acoustic analysis method. The developed system wasevaluated on a database constituted of music songs with Arabicspeech segments under several noisy environments.展开更多
An important concern with the deaf community is inability to hear partially or totally. This may affect the development of language during childhood, which limits their habitual existence. Consequently to facilitate s...An important concern with the deaf community is inability to hear partially or totally. This may affect the development of language during childhood, which limits their habitual existence. Consequently to facilitate such deaf speakers through certain assistive mechanism, an effort has been taken to understand the acoustic characteristics of deaf speakers by evaluating the territory specific utterances. Speech signals are acquired from 32 normal and 32 deaf speakers by uttering ten Indian native Tamil language words. The speech parameters like pitch, formants, signal-to-noise ratio, energy, intensity, jitter and shimmer are analyzed. From the results, it has been observed that the acoustic characteristics of deaf speakers differ significantly and their quantitative measure dominates the normal speakers for the words considered. The study also reveals that the informative part of speech in a normal and deaf speakers may be identified using the acoustic features. In addition, these attributes may be used for differential corrections of deaf speaker’s speech signal and facilitate listeners to understand the conveyed information.展开更多
In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless ...In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.展开更多
Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obs...Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.展开更多
Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this ...Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this paper, we propose a simple and novel method in Short Time Wavelet Packet (STWP) analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete cases. In this paper, the Laplacian model is considered in short time-wavelet packets and is applied to each histogram of packets. Expectation Maximization (EM) algorithm is used to train the model and calculate the model parameters. In our simulations, comparison with the other recent results will be computed and it is shown that our results are better than others. It is shown that complexity of computation of model is decreased and consequently the speed of convergence is increased.展开更多
Since Pulse Code Modulation emerged in 1937, digitized speech has experienced rapid development due to its outstanding voice quality, reliability, robustness and security in communication. But how to reduce channel wi...Since Pulse Code Modulation emerged in 1937, digitized speech has experienced rapid development due to its outstanding voice quality, reliability, robustness and security in communication. But how to reduce channel width without loss of speech quality remains a crucial problem in speech coding theory. A new full-duplex digital speech communication system based on the Vocoder of AMBE-1000(TM) and microcontroller ATMEL 89C51 is introduced. It shows higher voice quality than current mobile phone system with only a quarter of channel width needed for the latter. The prospective areas in which the system can be applied include satellite communication, IP Phone, virtual meeting and the most important, defence industry.展开更多
Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation...Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.展开更多
Research on the feature of speech and image signals are carried out from two perspectives,the time domain and the frequency domain.The speech and image signals are a non-stationary signal,so FT is not used for the non...Research on the feature of speech and image signals are carried out from two perspectives,the time domain and the frequency domain.The speech and image signals are a non-stationary signal,so FT is not used for the non-stationary characteristics of the signal.When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform(DFT).The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain.It has the problem of adjusting window size to a for desired resolution.But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities.This paper performs global processing speech encryption by combining speech with image of Fractional Fourier Transform.The speech signal is embedded watermark image that is processed by fractional transformation,and the embedded watermark has the effect of rotation and superposition,which improves the security of the speech.The paper results show that the proposed speech encryption method has a higher security level by Fractional Fourier Transform.The technology is easy to extend to practical applications.展开更多
Speech or Natural language contents are major tools of communication.This research paper presents a natural language processing based automated system for understanding speech language text.A new rule based model has ...Speech or Natural language contents are major tools of communication.This research paper presents a natural language processing based automated system for understanding speech language text.A new rule based model has been presented for analyzing the natural languages and extracting the relative meanings from the given text.User writes the natural language text in simple English in a few paragraphs and the designed system has a sound ability of analyzing the given script by the user.After composite analysis and extraction of associated information,the designed system gives particular meanings to an assortment of speech language text on the basis of its context.The designed system uses standard speech language rules that are clearly defined for all speech languages as English,Urdu,Chinese,Arabic,French,etc.The designed system provides a quick and reliable way to comprehend speech language context and generate respective meanings.展开更多
This paper studies two kinds of methods for pitch predictor in speech compressing coding, i.e., open-loop and closed-loop structures. Some of simplified approaches for solving pitch predictor equation are suggested, a...This paper studies two kinds of methods for pitch predictor in speech compressing coding, i.e., open-loop and closed-loop structures. Some of simplified approaches for solving pitch predictor equation are suggested, and the performances are compared under several conditions. The computer simulation results are shown.展开更多
Automatic speech recognition, often incorrectly called voice recognition, is a computer based software technique that analyzes audio signals captured by a microphone and translates them into machine interpreted text. ...Automatic speech recognition, often incorrectly called voice recognition, is a computer based software technique that analyzes audio signals captured by a microphone and translates them into machine interpreted text. Speech processing is based on techniques that need local CPU or cloud computing with an Internet link. An activation word starts the uplink;“OK google”, “Alexa”, … and voice analysis is not usually suitable for autonomous limited CPU system (16 bits microcontroller) with low energy. To achieve this realization, this paper presents specific techniques and details an efficiency voice command method compatible with an embedded IOT low-power device.展开更多
文摘Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.
文摘The acoustic characteristics or the chinese vowels of 24 children with cleft palate and 10 normal control children were analyzed by computerized speech signal processing system (CSSPS),and the speech articulation was judged with Glossary of clert palate speech(GCPS).The listening judgement showed that the speech articulation was significantly different between the two groups(P<0.01).The objective quantitative measurement suggested that the formant pattern(FP)of vowels in children with cleft palate was different from that of normal control children except vowel[a](P< 0.05).The acoustic vowelgraph or the Chinese vowels which demonstrated directly the relationship of vocal space and speech perception was stated with the first formant frequence(F1)and the second formant frequence(F2).The authors conclude that the values or F1 and F2 point out the upward and backward tongue movement to close the clert, which reflects the vocal characteristics of trausmission of clert palate speech.
文摘This paper proposes a multi-band speech enhancement algorithm exploiting iterative processing for enhancement of single channel speech. In the proposed algorithm, the output of the multi-band spectral subtraction (MBSS) algorithm is used as the input signal again for next iteration process. As after the first MBSS processing step, the additive noise transforms to the remnant noise, the remnant noise needs to be further re-estimated. The proposed algorithm reduces the remnant musical noise further by iterating the enhanced output signal to the input again and performing the operation repeatedly. The newly estimated remnant noise is further used to process the next MBSS step. This procedure is iterated a small number of times. The proposed algorithm estimates noise in each iteration and spectral over-subtraction is executed independently in each band. The experiments are conducted for various types of noises. The performance of the proposed enhancement algorithm is evaluated for various types of noises at different level of SNRs using, 1) objective quality measures: signal-to-noise ratio (SNR), segmental SNR, perceptual evaluation of speech quality (PESQ);and 2) subjective quality measure: mean opinion score (MOS). The results of proposed enhancement algorithm are compared with the popular MBSS algorithm. Experimental results as well as the objective and subjective quality measurement test results confirm that the enhanced speech obtained from the proposed algorithm is more pleasant to listeners than speech enhanced by classical MBSS algorithm.
文摘Sound indexing and segmentation of digital documentsespecially in the internet and digital libraries are very useful tosimplify and to accelerate the multimedia document retrieval. Wecan imagine that we can extract multimedia files not only bykeywords but also by speech semantic contents. The maindifficulty of this operation is the parameterization and modellingof the sound track and the discrimination of the speech, musicand noise segments. In this paper, we will present aSpeech/Music/Noise indexing interface designed for audiodiscrimination in multimedia documents. The program uses astatistical method based on ANN and HMM classifiers. After preemphasisand segmentation, the audio segments are analysed bythe cepstral acoustic analysis method. The developed system wasevaluated on a database constituted of music songs with Arabicspeech segments under several noisy environments.
文摘An important concern with the deaf community is inability to hear partially or totally. This may affect the development of language during childhood, which limits their habitual existence. Consequently to facilitate such deaf speakers through certain assistive mechanism, an effort has been taken to understand the acoustic characteristics of deaf speakers by evaluating the territory specific utterances. Speech signals are acquired from 32 normal and 32 deaf speakers by uttering ten Indian native Tamil language words. The speech parameters like pitch, formants, signal-to-noise ratio, energy, intensity, jitter and shimmer are analyzed. From the results, it has been observed that the acoustic characteristics of deaf speakers differ significantly and their quantitative measure dominates the normal speakers for the words considered. The study also reveals that the informative part of speech in a normal and deaf speakers may be identified using the acoustic features. In addition, these attributes may be used for differential corrections of deaf speaker’s speech signal and facilitate listeners to understand the conveyed information.
基金supported by the Human Sixth Sense Programme at the Advanced Digital Sciences Center from Singapore’s Agency for Science,Technology and Research
文摘In this paper, the frequency-domain Frost algorithm is enhanced by using conjugate gradient techniques for speech enhancement. Unlike the non-adaptive approach of computing the optimum minimum variance distortionless response （MVDR） solution with the correlation matrix inversion, the Frost algorithm implementing the stochastic constrained least mean square （LMS） algorithm can adaptively converge to the MVDR solution in mean-square sense, but with a very slow convergence rate. In this paper, we propose a frequency-domain constrained conjugate gradient （FDCCG） algorithm to speed up the convergence. The devised FDCCG algorithm avoids the matrix inversion and exhibits fast convergence. The speech enhancement experiments for the target speech signal corrupted by two and five interfering speech signals are demonstrated by using a four-channel acoustic-vector-sensor （AVS） micro-phone array and show the superior performance.
文摘Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.
文摘Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this paper, we propose a simple and novel method in Short Time Wavelet Packet (STWP) analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete cases. In this paper, the Laplacian model is considered in short time-wavelet packets and is applied to each histogram of packets. Expectation Maximization (EM) algorithm is used to train the model and calculate the model parameters. In our simulations, comparison with the other recent results will be computed and it is shown that our results are better than others. It is shown that complexity of computation of model is decreased and consequently the speed of convergence is increased.
文摘Since Pulse Code Modulation emerged in 1937, digitized speech has experienced rapid development due to its outstanding voice quality, reliability, robustness and security in communication. But how to reduce channel width without loss of speech quality remains a crucial problem in speech coding theory. A new full-duplex digital speech communication system based on the Vocoder of AMBE-1000(TM) and microcontroller ATMEL 89C51 is introduced. It shows higher voice quality than current mobile phone system with only a quarter of channel width needed for the latter. The prospective areas in which the system can be applied include satellite communication, IP Phone, virtual meeting and the most important, defence industry.
文摘Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.
基金The work is supported by Regional Innovation Cooperation Project of Sichuan Province(Grant No.22QYCX0082)Jian-Guo Wei received the grant,and the Science and Technology Plan of Qinghai Province,China(Grant No.2019-ZJ-7012)Xiu Juan Ma received the grant.
文摘Research on the feature of speech and image signals are carried out from two perspectives,the time domain and the frequency domain.The speech and image signals are a non-stationary signal,so FT is not used for the non-stationary characteristics of the signal.When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform(DFT).The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain.It has the problem of adjusting window size to a for desired resolution.But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities.This paper performs global processing speech encryption by combining speech with image of Fractional Fourier Transform.The speech signal is embedded watermark image that is processed by fractional transformation,and the embedded watermark has the effect of rotation and superposition,which improves the security of the speech.The paper results show that the proposed speech encryption method has a higher security level by Fractional Fourier Transform.The technology is easy to extend to practical applications.
文摘Speech or Natural language contents are major tools of communication.This research paper presents a natural language processing based automated system for understanding speech language text.A new rule based model has been presented for analyzing the natural languages and extracting the relative meanings from the given text.User writes the natural language text in simple English in a few paragraphs and the designed system has a sound ability of analyzing the given script by the user.After composite analysis and extraction of associated information,the designed system gives particular meanings to an assortment of speech language text on the basis of its context.The designed system uses standard speech language rules that are clearly defined for all speech languages as English,Urdu,Chinese,Arabic,French,etc.The designed system provides a quick and reliable way to comprehend speech language context and generate respective meanings.
文摘This paper studies two kinds of methods for pitch predictor in speech compressing coding, i.e., open-loop and closed-loop structures. Some of simplified approaches for solving pitch predictor equation are suggested, and the performances are compared under several conditions. The computer simulation results are shown.
文摘Automatic speech recognition, often incorrectly called voice recognition, is a computer based software technique that analyzes audio signals captured by a microphone and translates them into machine interpreted text. Speech processing is based on techniques that need local CPU or cloud computing with an Internet link. An activation word starts the uplink;“OK google”, “Alexa”, … and voice analysis is not usually suitable for autonomous limited CPU system (16 bits microcontroller) with low energy. To achieve this realization, this paper presents specific techniques and details an efficiency voice command method compatible with an embedded IOT low-power device.
