Deep neural networks for audio scene recognition

Yohan Petetin, Cyrille Laroche, Aurelien Mayoue

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

Abstract

These last years, artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), i.e. ANN with at least two hidden layers. In the same time, the computational auditory scene recognition (CASR) problem which consists in estimating the environment around a device from the received audio signal has been investigated. Most of works which deal with the CASR problem have tried to ind well-adapted features for this problem. However, these features are generally combined with a classical classi-ier. In this paper, we introduce DNN in the CASR ield and we show that such networks can provide promising results and perform better than standard classiiers when the same features are used.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages125-129
Number of pages5
ISBN (Electronic)9780992862633
DOIs
Publication statusPublished - 22 Dec 2015
Externally publishedYes
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 31 Aug 20154 Sept 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Conference

Conference23rd European Signal Processing Conference, EUSIPCO 2015
Country/TerritoryFrance
CityNice
Period31/08/154/09/15

Keywords

  • Deep neural networks
  • audio scene recognition
  • deep belief networks

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