Feature learning with deep scattering for urban sound analysis

Justin Salamon, Juan Pablo Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we evaluate the scattering transform as an alternative signal representation to the mel-spectrogram in the context of unsupervised feature learning for urban sound classification. We show that we can obtain comparable (or better) performance using the scattering transform whilst reducing both the amount of training data required for feature learning and the size of the learned codebook by an order of magnitude. In both cases the improvement is attributed to the local phase invariance of the representation. We also observe improved classification of sources in the background of the auditory scene, a result that provides further support for the importance of temporal modulation in sound segregation.

Original languageEnglish (US)
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages724-728
Number of pages5
ISBN (Electronic)9780992862633
DOIs
StatePublished - Dec 22 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: Aug 31 2015Sep 4 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period8/31/159/4/15

Keywords

  • Unsupervised learning
  • acoustic event classification
  • machine learning
  • scattering transform
  • urban

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint Dive into the research topics of 'Feature learning with deep scattering for urban sound analysis'. Together they form a unique fingerprint.

Cite this