Model-based Noise PSD Estimation from Speech in Non-stationary Noise

Jesper Kjær Nielsen, Mathew Shaji Kavalekalam, Mads Græsbøll Christensen, Jesper Bünsow Boldt

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

12 Citations (Scopus)
719 Downloads (Pure)

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 languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech, and Signal Processing
Number of pages5
Place of PublicationCalgary, Canada
PublisherIEEE
Publication date10 Sep 2018
Pages5424-5428
Article number8461683
ISBN (Electronic)978-1-5386-4658-8
DOIs
Publication statusPublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org/

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryCanada
CityCalgary
Period15/04/201820/04/2018
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Noise PSD estimation
  • Noise statistics
  • Speech enhancement

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