This paper describes two new algorithms for blind source separation (BSS) based on frequency-domain independent component analysis (FDICA). One is FDICA with pre-filtering by a speech sub-band passing filter to slow down the learning speed in low signal-to-noise ratio (SNR) sub-bands. The other is FDICA with sub-band selection learning to reduce the number of iterations for those sub-bands. The results of speech recognition experiments show that each method can improve word accuracy by as much as 7% and that the second method can increase the speed by approximately 60%.
Cite as: Saitoh, D., Kaminuma, A., Saruwatari, H., Nishikawa, T., Lee, A. (2005) Speech extraction in a car interior using frequency-domain ICA with rapid filter adaptations. Proc. Interspeech 2005, 2301-2304, doi: 10.21437/Interspeech.2005-736
@inproceedings{saitoh05_interspeech,
author={Daisuke Saitoh and Atsunobu Kaminuma and Hiroshi Saruwatari and Tsuyoki Nishikawa and Akinobu Lee},
title={{Speech extraction in a car interior using frequency-domain ICA with rapid filter adaptations}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2301--2304},
doi={10.21437/Interspeech.2005-736}
}