Skip to main navigation Skip to search Skip to main content

Overlapping sound event detection with supervised Nonnegative Matrix Factorization

  • Telecom Paris

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a supervised Nonnegative Matrix Factorization (NMF) model for overlapping sound event detection in real life audio. We start by highlighting the usefulness of non-euclidean NMF to learn representations for detecting and classifying acoustic events in a multi-label setting. Then, we propose to learn a classifier and the NMF decomposition in a joint optimization problem. This is done with a general β-divergence version of the nonnegative task-driven dictionary learning model. An experimental evaluation is performed on the development set of the DCASE 2016 task3 challenge. The proposed supervised NMF-based system improves performance over the baseline and the submitted systems.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-35
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Acoustic Event Detection
  • Nonnegative Matrix Factorization
  • Supervised Feature learning

Fingerprint

Dive into the research topics of 'Overlapping sound event detection with supervised Nonnegative Matrix Factorization'. Together they form a unique fingerprint.

Cite this