Real-time integration of statistical model-based speech enhancement with unsupervised noise PSD estimation using microphone array
Tomoko Kawase, Kenta Niwa, Masakiyo Fujimoto, Noriyoshi Kamado, Kazunori Kobayashi, Shoko Araki, Tomohiro Nakatani
Abstract
We propose a technique of multi-channel speech enhancement based on integration of beamforming and statistical model-based speech enhancement to clearly extract the target speech, even in very noisy environments. Conventional microphone array-based techniques estimate speech and noise power spectral densities (PSDs) from the spatial cues of the sound sources; however, their estimation errors dramatically increase when there are many noise sources. We integrated clean speech models trained in advance and the noise PSDs estimated in beamspace to compose observation models and designed a precise Wiener filter. Experiments under adverse noise conditions showed that the proposed technique significantly improved the signal-to-noise ratios (SNRs) compared with the conventional microphone array processing technique.
BibTeX
@inproceedings{icassp2016_realtimeintegrat,
title = {Real-time integration of statistical model-based speech enhancement with unsupervised noise PSD estimation using microphone array},
author = {Tomoko Kawase and Kenta Niwa and Masakiyo Fujimoto and Noriyoshi Kamado and Kazunori Kobayashi and Shoko Araki and Tomohiro Nakatani},
booktitle = {ICASSP 2016},
year = {2016}
}