Trainable COPE Features for Sound Event Detection

Nicola Strisciuglio*, Nicolai Petkov

*Corresponding author for this work

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

Abstract

Systems for automatic analysis of sounds and detection of events are of great importance as they can be used as substitutes of or complement to video analytic systems. In this paper we describe a flexible system for the detection of audio events based on the use of trainable COPE (Combination of Peaks of Energy) features. The structure of a COPE feature is determined in an automatic configuration process on a single prototype example. Thus, they can be adapted to different kinds of sounds of interest. We configure a set of COPE features in order to account for robustness to variations of the characteristics of sounds within a specific class. The proposed system is flexible as new features (also configured on examples drawn from new classes) can be easily added to the feature set. We performed experiments on the MIVIA road events data set for road surveillance applications and compared the results that we achieved with the ones of other existing methods.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Subtitle of host publication24th Iberoamerican Congress
EditorsIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
PublisherSpringer
Pages599-609
Number of pages11
ISBN (Electronic)978-3-030-33904-3
ISBN (Print)978-3-030-33903-6
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 - Havana, Cuba
Duration: 28 Oct 201931 Oct 2019
Conference number: 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11896 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Iberoamerican Congress on Pattern Recognition, CIARP 2019
Abbreviated titleCIARP 2019
CountryCuba
CityHavana
Period28/10/1931/10/19

Keywords

  • Few shot training
  • Sound event detection
  • Trainable features

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