Integrated approach of feature extraction and sound source enhancement based on maximization of mutual information
Yuma Koizumi, Kenta Niwa, Yusuke Hioka, Kazunori Kobayashi, Hitoshi Ohmuro
Abstract
We investigated informative acoustic feature extraction based on dimension reduction for collecting target sources on a noisy sports field. Although a Wiener filter is often used for sound source enhancement, it is difficult to accurately design the Wiener filter by simply using spatial cues because the noise on a sports field (e.g., cheering from spectators) arrives from the same direction as that of the targeted source. A statistical approach is used to estimate the Wiener filter by using pre-trained acoustic feature models. However, an informative acoustic feature, which provides a powerful clue for clear extraction of the target source, is unknown. For this study, we developed a method for optimizing a projection matrix for dimension reduction by maximizing the mutual information between acoustic features and the Wiener filter. Through experiments using two-directional microphones on a mock sports field, we confirmed that the proposed method outperformed previous methods in terms of both the noise reduction and quality of the recovered sound sources.
BibTeX
@inproceedings{icassp2016_integratedapproa,
title = {Integrated approach of feature extraction and sound source enhancement based on maximization of mutual information},
author = {Yuma Koizumi and Kenta Niwa and Yusuke Hioka and Kazunori Kobayashi and Hitoshi Ohmuro},
booktitle = {ICASSP 2016},
year = {2016}
}