A well known technique for speech enhancement is spectral subtraction. This technique is attractive due to its simplicity and its low computational cost. However, spectral subtraction has the drawback of generating residual noise with a musical character, the so-called musical noise. In this paper, we propose a simple modi cation of the lter coe cient calculation in form of a recursion of the p...

The objective of Speech Enhancement is to improve the quality of audio signal that are degraded speech signal used in various application i.e. VOIP, hearing aids, teleconferencing systems. Currently the Speech Enhancement deals with several problems to improve requirements of speech recognition such as background noise that is presented in speech signal. The proposed work introduces alternative...

The one-microphone speech enhancement, (SE) algorithm as a modification of the extended spectral subtraction (SS) method (ESS) [1] is presented in this paper. The algorithm can be used for the reduction of additive stationary and quasi-stationary colored broad-band noise in noisy speech in hands-free communication terminals as well as in a number of other applications. The achieved noise reduct...

This paper proposes a new enhancement technique for the enhancement of broadband noise corrupted speech. The technique exploits the human auditory systems inability to distinguish between individual frequency components within critical frequency bands. Spectral subtraction is used and the spectrum is considered as critical frequency bands rather than individual frequency components. The propose...

In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non st...

In this paper we present a new statistical model, which describes the corruption to speech recognition Mel-frequency spectral features caused by additive noise. This model explicitly represents the effect of unknown phase together with the unobserved clean speech and noise as three hidden variables. We use this model to produce noise robust features for automatic speech recognition. The model i...

In this paper we investigate the enhancement of speech by applying MMSE short-time spectral magnitude estimation in the modulation domain. For this purpose, the traditional analysismodification-synthesis framework is extended to include modulation domain processing. We compensate the noisy modulation spectrum for additive noise distortion by applying the MMSE short-time spectral magnitude estim...