The challenging problem of online multi-microphone blind audio source separation (BASS) in noisy environment is addressed in this paper. We present a sequential, non-iterative, algorithm based on the recursive EM (REM) framework. In the proposed algorithm, the compete-data, which constitutes the separated sources and residual noise, is estimated in the E-step by applying a multichannel Wiener filter (MCWF); and the corresponding parameters, comprised of acoustic transfer functions (ATFs) relating the sources and the microphones and power spectral densities (PSDs) of the desired sources, are sequentially estimated in the M-step. The separated speech signals are further enhanced using matched-filter beamformers. The performance of the algorithm is demonstrated in terms of the separation capabilities, the resulting speech intelligibility and the ability to track the direction of arrival (DOA) of the moving sources.
|Title of host publication||22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021|
|Publisher||International Speech Communication Association|
|Number of pages||5|
|State||Published - 2021|
|Event||22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic|
Duration: 30 Aug 2021 → 3 Sep 2021
|Name||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|Conference||22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021|
|Period||30/08/21 → 3/09/21|
Bibliographical noteFunding Information:
The project received funding from the EU Horizon 2020 Research and Innovation Programme, Grant Agreement #871245.
Copyright © 2021 ISCA.
- Multichannel Wiener filter beamforming
- Recursive expectation maximization
- blind audio source separation