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Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement  [PDF]
Erhan Deger,Md. Khademul Islam Molla,Keikichi Hirose,Nobuaki Minematsu,Md. Kamrul Hasan
Advances in Acoustics and Vibration , 2014, DOI: 10.1155/2014/765454
Abstract: This paper presents a two-stage soft thresholding algorithm based on discrete cosine transform (DCT) and empirical mode decomposition (EMD). In the first stage, noisy speech is decomposed into eight frequency bands and a specific noise variance is calculated for each one. Based on this variance, each band is denoised using soft thresholding in DCT domain. The remaining noise is eliminated in the second stage through a time domain soft thresholding strategy adapted to the intrinsic mode functions (IMFs) derived by applying EMD on the signal obtained from the first stage processing. Significantly better SNR improvement and perceptual speech quality results for different noise types prove the superiority of the proposed algorithm over recently reported techniques. 1. Introduction In many speech related systems, the desired signal is not available directly; rather it is mostly contaminated with some interference sources. These background noise signals degrade the quality and intelligibility of the original speech, resulting in a severe drop in the performance of the post applications. Speech enhancement aims at improving the perceptual quality and intelligibility of such speech signals degraded in noisy environments, mainly through noise reduction algorithms [1]. Due to its significant importance in today’s information technology, many methods have been developed for this purpose. A major problem in most algorithms is that the enhanced speech signal has distortions compared to the original one which results in loss of some speech details. The residual noise is another problem which affects the performance of the postprocessing systems. Soft thresholding is a powerful technique used for removing the noise components by subtracting a constant value from the coefficients of the noisy speech signal obtained by the analyzing transformation. However, such type of direct subtraction results in a degradation of the speech components. Unlike the conventional constant noise-level subtraction rule [2, 3], a new soft thresholding strategy based on frequency frames was proposed in [4]. The later one is able to remove the noise components while giving significantly less damage to the speech signal. This enables even signals with high SNRs to be processed effectively. However due to the thresholding criteria, a noticeable amount of noise still remains in the enhanced signal. Another disadvantage is the lack of robustness of the algorithm to different noise types. The empirical mode decomposition (EMD), recently pioneered by Huang et al. [5] as a new and powerful data
Single-Channel Speech Enhancement by NWNS and EMD  [PDF]
Somlal Das,Mohammad Ekramul Hamid,Keikichi Hirose,Md. Khademul Islam Molla
Signal Processing : An International Journal , 2010,
Abstract: This paper presents the problem of noise reduction from observed speech by means of improving quality and/or intelligibility of the speech using single-channel speech enhancement method. In this study, we propose two approaches for speech enhancement. One is based on traditional Fourier transform using the strategy of Noise Subtraction (NS) that is equivalent to Spectral Subtraction (SS) and the other is based on the Empirical Mode Decomposition (EMD) using the strategy of adaptive thresholding. First of all, the two different methods are implemented individually and observe that, both the methods are noise dependent and capable to enhance speech signal to a certain limit. Moreover, traditional NS generates unwanted residual noise as well. We implement nonlinear weight to eliminate this effect and propose Nonlinear Weighted Noise Subtraction (NWNS) method. In first stage, we estimate the noise and then calculate the Degree Of Noise (DON1) from the ratio of the estimated noise power to the observed speech power in frame basis for different input Signal-to-Noise-Ratio (SNR) of the given speech signal. The noise is not accurately estimated using Minima Value Sequence (MVS). So the noise estimation accuracy is improved by adopting DON1 into MVS. The first stage performs well for wideband stationary noises and performed well over wide range of SNRs. Most of the real world noise is narrowband non-stationary and EMD is a powerful tool for analyzing non-linear and non-stationary signals like speech. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode function (IMFs). Since the IMFs having different noise and speech energy distribution, hence each IMF has a different noise and speech variance. These variances change for different IMFs. Therefore an adaptive threshold function is used, which is changed with newly computed variances for each IMF. In the adaptive threshold function, adaptation factor is the ratio of the square root of added noise variance to the square root of estimated noise variance. It is experimentally observed that the better speech enhancement performance is achieved for optimum adaptation factor. We tested the speech enhancement performance using only EMD based adaptive thresholding method and obtained the outcome only up to a certain limit. Therefore, further enhancement from the individual one, we propose two-stage processing technique, NWNS+EMD. The first stage is used as a pre-process for noise removal to a certain level resulting first enhanced speech and placed this into second stage for furthe
Speech Enhancement via EMD  [cached]
Kais Khaldi,Abdel-Ouahab Boudraa,Abdelkhalek Bouchikhi,Monia Turki-Hadj Alouane
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/873204
Abstract: In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.
Noisy Speech Enhancement Using Soft Thresholding on Selected Intrinsic Mode Functions  [PDF]
Hadhami Issaoui , A?cha Bouzid, Noureddine Ellouze
Signal Processing : An International Journal , 2011,
Abstract: In this paper, a new speech enhancement method is introduced. It is essentially based on theEmpirical Mode Decomposition technique (EMD) and a soft thresholding approach applied onselected modes. The proposed method is a fully data driven approach. First the noisy speechsignal is decomposed adaptively into intrinsic oscillatory components called Intrinsic ModeFunctions (IMFs) by using a time decomposition called sifting process. Second, selected IMFsare soft thresholded and added to the remaining IMFs with the residue to reconstitute theenhanced speech signal. The proposed approach is evaluated using speech signals fromNOISEUS database corrupted with additive white Gaussian noise. Our algorithm is compared toother state of the art algorithms.
Teager Energy Operation on Wavelet Packet Coefficients for Enhancing Noisy Speech Using a Hard Thresholding Function  [PDF]
Tahsina Farah Sanam,Celia Shahnaz
Signal Processing : An International Journal , 2012,
Abstract: In this paper a new thresholding based speech enhancement approach is presented, where thethreshold 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 ofthe 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 evaluatethe performance of the proposed method. Standard objective measures, spectrogram representationsand subjective listening tests show that the proposed method outperforms the existing state-of-the-artthresholding based speech enhancement approaches for noisy speech from high to low levels ofSNR.
Wavelet-Based Speech Enhancement Using Time-Frequency Adaptation  [cached]
Kun-Ching Wang
EURASIP Journal on Advances in Signal Processing , 2009, DOI: 10.1155/2009/924135
Abstract: Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient threshold (WCT) based on time-frequency adaptation is proposed. In addition, an unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. The wavelet coefficient threshold (WCT) of each subband is first temporally adjusted according to the value of a posterior signal-to-noise ratio (SNR). To prevent the degradation of unvoiced sounds during noise, the algorithm utilizes a simple speech/noise detector (SND) and further divides speech signal into unvoiced and voiced sounds. Then, we apply appropriate wavelet thresholding according to voiced/unvoiced (V/U) decision. Based on the masking properties of human auditory system, a perceptual gain factor is adopted into wavelet thresholding for suppressing musical residual noise. Simulation results show that the proposed method is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods.
Processing Noisy Speech for Enhancement
Krishnamoorthy P,Prasanna Mahadeva
IETE Technical Review , 2007,
Abstract: The main objective of this paper is to provide an overview of the major techniques that have been proposed for enhancement of noisy speech. This paper also proposes a method for enhancement of noisy speech. The proposed method is motivated as an attempt to minimize the limitations of conventional spectral subtraction method for enhancement of noisy speech. The proposed method involves three steps. In the first step speech and non-speech regions are detected from the degraded speech signal. In the second step, for each speech region, noise components are estimated from the preceding noise regions and are subtracted by conventional spectral subtraction method. In the third step, speech components are enhanced further from the spectral subtracted speech signal for reducing the musical noise, which is the artifact of spectral subtraction method. The processed speech signals from the proposed method seem to be better perceptually compared to that of the spectral subtraction method.
Neural Network Based Speech Enhancement
J. Tlucak,J. Juhar,L. Dobos,A. Cizmar
Radioengineering , 1999,
Abstract: This paper deals with methods of speech enhancement with particular focus on neural speech enhancement. Speech enhancement is concerned with the neural processing of noisy speech to improve the quality and intelligibility of the speech signal. The goal of this paper is to describe an experiment with implementation of two channel adaptive noise canceler via direct time domain mapping approach.
Enhancement of Speech in Noisy Conditions  [PDF]
International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering , 2013,
Abstract: The term “Speech Enhancement” refereed as to improve quality or intelligibility of speech signal. Speech signal is often degraded by additive background noise like babble noise, train noise, restaurant noise etc. In such noisy environment listening task is very difficult at the end user. Many times speech enhancement is used for pre processing of speech for computer speech recognition system. This paper presents speech enhancement methods like Spectral Subtraction, Modified Spectral Subtraction and Least Mean Square to reduce additive background noise. Basically these methods are single channel speech enhancement methods. The performance of SS algorithm and LMS algorithm is evaluated by object speech measure like, Signal to Noise Ratio, Mean Square Error, Root Mean Square Error and Normalized Root Mean Square. From result we conclude that the performance of SS algorithm and Modified SS algorithm is better than LMS algorithm. So SS algorithm is widely used in personal communication due its simplicity.
Image Enhancement by Thresholding on Wavelet Coefficient  [PDF]
M. E. Akbarpour,M. R. Karami Mollaei
International Journal of Soft Computing & Engineering , 2012,
Abstract: The different wavelet transform-based methods of theimage De-noising by thresholding on wavelet coefficients arediscussed in this paper.These methods include different ways of adaptive calculating ofthreshold value and also kinds of thresholdig function. Afterexamining the existing methods, a simple and efficient methodbased on local features of each pixel has been proposed. At last theproposed method has been compared the other existing methodsand it is obtained that the proposed method, despite of thesimplicity, has the same efficiency as some of the commoncomplex methods. In addition some times it has better responsethan the complex methods.
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