A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise
2018
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay
doi:10.1109/icassp.2018.8461683
dblp:conf/icassp/NielsenKCB18
fatcat:r2pgt6ixfff27olbzhu5nzsery