scholarly journals Speech Enhancement Using Minimum Mean-Square Error Amplitude Estimators Under Normal and Generalized Gamma Distribution

2010 ◽  
Vol 6 (7) ◽  
pp. 700-705 ◽  
Author(s):  
Boubakir
Keyword(s):  
Mean Square Error ◽  
Gamma Distribution ◽  
Speech Enhancement ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma

scholarly journals An Improved MMSE Amplitude Estimator under Generalized Gamma Distribution Based on Auditory Perception

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Chabane Boubakir ◽  
Daoud Berkani
Keyword(s):  
Gamma Distribution ◽  
Speech Enhancement ◽  
Minimum Mean Square Error ◽  
Masking Effect ◽  
Spectral Amplitude ◽  
Auditory Masking ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma ◽  
Mmse Estimator ◽  
Short Time

This paper describes a new speech enhancement approach which employs the minimum mean square error (MMSE) estimator based on the generalized gamma distribution of the short-time spectral amplitude (STSA) of a speech signal. In the proposed approach, the human perceptual auditory masking effect is incorporated into the speech enhancement system. The algorithm is based on a criterion by which the audible noise may be masked rather than being attenuated, thereby reducing the chance of speech distortion. Performance assessment is given to show that our proposal can achieve a more significant noise reduction as compared to the perceptual modification of Wiener filtering and the gamma based MMSE estimator.

scholarly journals Quality Evaluation of Speech Enhancement Algorithms for Normal and Hearing Loss Listeners

2019 ◽  
Vol 8 (12) ◽  
pp. 7-12
Keyword(s):  
Mean Square Error ◽  
Speech Enhancement ◽  
Quality Evaluation ◽  
Minimum Mean Square Error ◽  
Subjective Quality ◽  
Binary Mask ◽  
Mean Square ◽  
Regional Language ◽  
Speech Database ◽  
Ideal Binary Mask

The subjective quality test of the enhanced speech from different enhancement algorithms for listeners with normal hearing (NH) capability as well as listeners with hearing impairment (HI) is reported. The subjective quality evaluation of speech enhancement methods in the literature survey is mostly done targeting NH listeners and fewer attempts are observed to subjectively evaluate for HI listeners. The algorithms evaluated are from four different classes: spectral subtraction class(SS), statistical model based class (minimum mean square error), subspace class(PKLT) and auditory class (ideal binary mask using STFT, ideal binary mask using gammatone filterbank and ideal binary mask using gammachirp filterbank). The algorithms are evaluated using four types of real world noises recorded in Indian scenarios namely cafeteria, traffic, station and train at -5, 0, 5 and 10 dB SNRs. The evaluation is being done as per ITU-T P.835 standard in terms of three parametersspeech signal alone, background noise and overall quality. The noisy speech database developed in Indian regional language, Marathi, at four SNRs -5, 0, 5 and 10 dB is used for evaluation. Significant improvement is observed in ideal binary mask algorithm in terms of overall quality and signal distortion ratings for NH and HI listeners. The performance of minimum mean square error is also observed comparable with the ideal binary mask algorithm in some cases.

scholarly journals GUI Based Execution of Discourse Upgrade System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2151-2155
Keyword(s):  
Mean Square Error ◽  
Speech Enhancement ◽  
Minimum Mean Square Error ◽  
Spectral Subtraction ◽  
Mean Square

Discourse, being a key method for correspondence, has been inserted in different applications. In numerous unavoidable circumstances, we are rendered vulnerable attempting to conclude the understandability of the discourse and this is the place Speech improving strategy i.e. evacuation of undesirable foundation commotion, comes into picture. In this paper, an endeavor has been made towards contemplating Speech Enhancement methods, for example, Spectral Subtraction, Minimum Mean Square Error (MMSE), Kalman and Wiener channel. In view of our perceptions and investigation of different execution parameters, we finish up which of the strategies is most reasonable for discourse improvement. The usage of the code for different channels is finished utilizing.

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