Auditory feature for monaural speech segregation

DOI

10.2991/icieac-14.2014.16How to use a DOI?

Keywords

gammatone frequency cepstral coefficients (GFCC); monaural speech segregation; binary classification; time-frequency(T-F) unit

Abstract

Monaural speech segregation has been a very challenging problem for speech signal processing. The implication of the ideal binary masks to an auditory mixture has been shown to yield substantial improvements in signal-to-noise-ratio (SNR) and intelligibility. In this paper, we use the time-frequency (T-F) unit level gammatone frequency cepstral coefficients (GFCC) auditory feature to estimate the ideal binary mask for monaural speech segregation. The paper reports the successful attempt to use GFCC as the segregation cue with deep neural networks (DNNs) classifier. Results show that robust performance can be achieved across noisy and reverberant conditions.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yi Jiang
AU  - RunshenG Liu
AU  - Yuanyuan Zu
PY  - 2014/03
DA  - 2014/03
TI  - Auditory feature for monaural speech segregation
BT  - Proceedings of the 2nd International Conference on Information, Electronics and Computer
PB  - Atlantis Press
SP  - 69
EP  - 72
SN  - 1951-6851
UR  - https://doi.org/10.2991/icieac-14.2014.16
DO  - 10.2991/icieac-14.2014.16
ID  - Jiang2014/03
ER  -