Abstract
| Original language | English |
|---|---|
| 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 Sep 2018 |
| Pages | 5424-5428 |
| Article number | 8461683 |
| ISBN (Electronic) | 978-1-5386-4658-8 |
| DOIs | |
| Publication status | Published - 10 Sep 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) |
|---|---|
| Country | 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 |
|---|---|
| ISSN | 1520-6149 |
Fingerprint
Keywords
- Noise PSD estimation
- Noise statistics
- Speech enhancement
Cite this
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Model-based Noise PSD Estimation from Speech in Non-stationary Noise. / Nielsen, Jesper Kjær; Kavalekalam, Mathew Shaji; Christensen, Mads Græsbøll; Boldt, Jesper Bünsow.
IEEE International Conference on Acoustics, Speech, and Signal Processing. Calgary, Canada : IEEE, 2018. p. 5424-5428 8461683 (I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings).Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
TY - GEN
T1 - Model-based Noise PSD Estimation from Speech in Non-stationary Noise
AU - Nielsen, Jesper Kjær
AU - Kavalekalam, Mathew Shaji
AU - Christensen, Mads Græsbøll
AU - Boldt, Jesper Bünsow
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
KW - Noise PSD estimation
KW - Noise statistics
KW - Speech enhancement
UR - http://www.scopus.com/inward/record.url?scp=85054270558&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461683
DO - 10.1109/ICASSP.2018.8461683
M3 - Article in proceeding
T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
SP - 5424
EP - 5428
BT - IEEE International Conference on Acoustics, Speech, and Signal Processing
PB - IEEE
CY - Calgary, Canada
ER -