Abstract
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 to give good results in non- stationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estima- tors in terms of tracking speed and estimation accuracy.
Original language | English |
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Title of host publication | IEEE International Conference on Acoustics, Speech, and Signal Processing |
Number of pages | 5 |
Place of Publication | Calgary, Canada |
Publisher | IEEE |
Publication date | 10 Sept 2018 |
Pages | 5424-5428 |
Article number | 8461683 |
ISBN (Electronic) | 978-1-5386-4658-8 |
DOIs | |
Publication status | Published - 10 Sept 2018 |
Event | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 https://2018.ieeeicassp.org/ |
Conference
Conference | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Country/Territory | Canada |
City | Calgary |
Period | 15/04/2018 → 20/04/2018 |
Internet address |
Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
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ISSN | 1520-6149 |
Keywords
- Noise PSD estimation
- Noise statistics
- Speech enhancement