Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

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

ICASSP 2018accepted0 citations

Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

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

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.

BibTeX

@inproceedings{icassp2018_modelbasednoisep,
  title = {Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise},
  author = {Jesper Kjær Nielsen and Mathew Shaji Kavalekalam and Mads Græsbøll Christensen and Jesper Bünsow Boldt},
  booktitle = {ICASSP 2018},
  year = {2018}
}