The performance of Wiener filters in restoring the quality and intelligibility of noisy speech depends on: (i) the accuracy of the estimates of the power spectra or the correlation values of the noise and the speech processes, and (ii) on the Wiener filter structure. In this paper a Bayesian method is proposed where model combination and model decomposition are employed for the estimation of parameters required to implement subband Wiener filters. The use of subband Wiener filters provides advantages in terms of improved parameter estimates and also in restoring the temporal-spectral composition of speech. The method is evaluated, and compared with the parallel model combination, using the TIMIT continuous speech database with BMW and VOLVO car noise databases.
Cite as: Chen, A., Vaseghi, S. (2000) State based sub-band Wiener filters for speech enhancement in car environments. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 1125-1128, doi: 10.21437/ICSLP.2000-734
@inproceedings{chen00k_icslp,
author={Aimin Chen and Saeed Vaseghi},
title={{State based sub-band Wiener filters for speech enhancement in car environments}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 3, 1125-1128},
doi={10.21437/ICSLP.2000-734}
}