Post-processing for frequency-domain blind source separation in hearing aids

Authors:
Shaobo Han;Jie Cui;Ping Li
Affiliations:
Laboratory of Medical Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China;Laboratory of Medical Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China;Laboratory of Medical Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China
Venue:
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Year:
2009

Citing 6
Cited 1

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
The Cocktail Party Problem

Neural Computation
Adaptive β-order generalized spectral subtraction for speech enhancement

Signal Processing
Enhancement of speech signals separated from their convolutive mixture by FDICA algorithm

Digital Signal Processing
An objective measure for the musical noise assessment in noise reduction systems

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Musical noise generation analysis for noise reduction methods based on spectral subtraction and MMSE STSA estimation

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing

A multistage approach to blind separation of convolutive speech mixtures

Speech Communication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we investigate post-processing for the frequency-domain blind source separation (FD-BSS) in hearing aids applications. It is known that the segregate quality of FD-BSS degrades severely in the challenging scenario of reverberant enclosures or moving source situations. A robust two-stage dynamic programming approach based on inter-frequency correlation is presented to solving the permutation ambiguity correction problem. Moreover, binary masking method and the non-stationary spectral subtraction techniques are combined to estimate and reduce the residual cross-talk components. The subtraction parameter is adaptively determined by estimated local Signal-to-Noise Ratio (SNR). An optimization scheme is proposed aiming at obtaining a tradeoff between the residual noise reduction and generated distortion suppression. Experimental results show that the post-processing procedure enhances the separated signals and the musical noise is controlled under moderate level.