Conference paper
A novel binary mask estimator based on sparse approximation
While most single-channel noise reduction algorithms fail to improve speech intelligibility, the ideal binary mask (IBM) has demonstrated substantial intelligibility improvements. However, this approach exploits oracle knowledge. The main objective of this paper is to introduce a novel binary mask estimator based on a simple sparse approximation algorithm.
Our approach does not require oracle knowledge and instead uses knowledge of speech structure.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2013 |
| Pages: | 7497-7501 |
| Proceedings: | ICASSP 2013 - IEEE International Conference on Acoustics, Speech and Signal Processing |
| ISBN: | 1479903566 and 9781479903566 |
| ISSN: | 2379190x and 15206149 |
| Types: | Conference paper |
| DOI: | 10.1109/ICASSP.2013.6639120 |
Approximation methods Ideal binary mask Matching pursuit algorithms Signal processing algorithms Signal to noise ratio Speech Time-frequency analysis approximation theory binary mask estimator intelligibility noise reduction oracle knowledge single-channel noise reduction algorithm sparse approximation speech intelligibility time-frequency masking
