Speech Communication - Special issue on speech processing in adverse conditions
Speech recognition in noisy environments: a survey
Speech Communication
Speech recognition by machines and humans
Speech Communication
Environmental conditions and acoustic transduction in hands-free speech recognition
Speech Communication - Special issue on robust speech recognition
Independent component analysis: algorithms and applications
Neural Networks
Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Neural Computation
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Speech Enhancement (Signals and Communication Technology)
Speech Enhancement (Signals and Communication Technology)
Indeterminacy free frequency-domain blind separation of reverberant audio sources
IEEE Transactions on Audio, Speech, and Language Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
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In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.