Online Parametric NMF for Speech Enhancement

Mathew Shaji Kavalekalam, Jesper Kjær Nielsen, Liming Shi, Mads Græsbøll Christensen, Jesper Boldt

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

2 Citations (Scopus)
191 Downloads (Pure)


In this paper, we propose a speech enhancement method based on non-negative matrix factorization (NMF) techniques. NMF techniques allow us to approximate the power spectral density (PSD) of the noisy signal as a weighted linear combination of trained speech and noise basis vectors arranged as the columns of a matrix. In this work, we propose to use basis vectors that are parameterised by autoregressive (AR) coefficients. Parametric representation of the spectral basis is beneficial as it can encompass the signal characteristics like, e.g. the speech production model. It is observed that the parametric representation of basis vectors is beneficial while performing online speech enhancement in low delay scenarios.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
Number of pages5
Publication date2018
Article number8553039
ISBN (Print)978-90-827970-0-8
ISBN (Electronic)978-9-0827-9701-5
Publication statusPublished - 2018
Event26th European Signal Processing Conference (EUSIPCO 2018) - Rome, Italy
Duration: 3 Sep 20187 Sep 2018
Conference number: 26


Conference26th European Signal Processing Conference (EUSIPCO 2018)
Internet address
SeriesProceedings of the European Signal Processing Conference


  • autoregressive modelling, speech enhancement, NMF

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