Configurable Digital Hearing Aid System with Reduction of Noise for Speech Enhancement Using Spectral Subtraction Method and Frequency Dependent Amplification

Author(s):  
Biswajit Saha ◽  
Shakil Khan ◽  
Celia Shahnaz ◽  
Shaikh Anowarul Fattah ◽  
Mohammad Tariqul Islam ◽  
...  
2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Sheng Li ◽  
Jian Qi Wang ◽  
Xi Jing Jing

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.


2020 ◽  
Vol 123 ◽  
pp. 35-42
Author(s):  
Xue Yan ◽  
Zhen Yang ◽  
Tingting Wang ◽  
Haiyan Guo

2010 ◽  
Vol 19 (02) ◽  
pp. 159-173 ◽  
Author(s):  
IOSIF MPORAS ◽  
TODOR GANCHEV ◽  
OTILIA KOCSIS ◽  
NIKOS FAKOTAKIS

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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