A modified spectral subtraction method for speech enhancement based on masking property of human auditory system

2009 ◽  
Author(s):  
Bing-yin Xia ◽  
Yan Liang ◽  
Chang-chun Bao
Keyword(s):  
Auditory System ◽  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Subtraction Method ◽  
Human Auditory System ◽  
Spectral Subtraction Method

scholarly journals The Application of Nonlinear Spectral Subtraction Method on Millimeter Wave Conducted Speech Enhancement

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Sheng Li ◽  
Jian Qi Wang ◽  
Xi Jing Jing
Keyword(s):  
Millimeter Wave ◽  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Frequency Noise ◽  
Distribution Analysis ◽  
Subtraction Method ◽  
Time Frequency ◽  
Perceptual Evaluation ◽  
Reduction Effect ◽  
Spectral Subtraction Method

A nonlinear multiband spectral subtraction method is investigated in this study to reduce the colored electronic noise in millimeter wave (MMW) radar conducted speech. Because the over-subtraction factor of each Bark frequency band can be adaptively adjusted, the nonuniform effects of colored noise in the spectrum of the MMW radar speech can be taken into account in the enhancement process. Both the results of the time-frequency distribution analysis and perceptual evaluation test suggest that a better whole-frequency noise reduction effect is obtained, and the perceptually annoying musical noise was efficiently reduced, with little distortion to speech information as compared to the other standard speech enhancement algorithm.

Improved Spectral Subtraction Speech Enhancement Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 536-541 ◽  
Author(s):  
Yu Hong Liu ◽  
Dong Mei Zhou ◽  
Zhan Jun Jiang
Keyword(s):  
Auditory System ◽  
Speech Enhancement ◽  
Critical Frequency ◽  
Spectral Subtraction ◽  
Human Auditory System ◽  
Speech Distortion ◽  
Simulation Results ◽  
Enhancement Algorithm ◽  
Output Snr

The paper addresses the problems of speech distortion and residual musical noise introduced by conventional spectral subtraction (SS) method for speech enhancement. In this paper, we propose a modified SS algorithm for speech enhancement based on the masking properties of human auditory system. The algorithm computes the parameters α and β dynamically according to the masking thresholds of the critical frequency segments for each speech frame. Simulation results show that the proposed algorithm is superior to the conventional SS method, not only in the improvement of output SNR, but in the reduction of the speech distortion and residual musical noise.

scholarly journals An Iterative Graph Spectral Subtraction Method for Speech Enhancement

2020 ◽  
Vol 123 ◽  
pp. 35-42
Author(s):  
Xue Yan ◽  
Zhen Yang ◽  
Tingting Wang ◽  
Haiyan Guo
Keyword(s):  
Speech Enhancement ◽  
Spectral Subtraction ◽  
Subtraction Method ◽  
Spectral Subtraction Method

SPEECH ENHANCEMENT FOR ROBUST SPEECH RECOGNITION IN MOTORCYCLE ENVIRONMENT

2010 ◽  
Vol 19 (02) ◽  
pp. 159-173 ◽  
Author(s):  
IOSIF MPORAS ◽  
TODOR GANCHEV ◽  
OTILIA KOCSIS ◽  
NIKOS FAKOTAKIS
Keyword(s):  
Speech Recognition ◽  
Speech Enhancement ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Human Perception ◽  
Spectral Subtraction ◽  
Robust Speech Recognition ◽  
Subtraction Method ◽  
Stationary Conditions ◽  
Spectral Subtraction Method

In the present work, we investigate the performance of a number of traditional and recent speech enhancement algorithms in the adverse non-stationary conditions, which are distinctive for motorcycles on the move. The performance of these algorithms is ranked in terms of the improvement they contribute to the speech recognition accuracy, when compared to the baseline performance, i.e. without speech enhancement. The experiments on the MoveOn motorcycle speech and noise database indicated that there is no equivalence between the ranking of algorithms based on the human perception of speech quality and the speech recognition performance. The Multi-band spectral subtraction method was observed to lead to the highest speech recognition performance.

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