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

1 Citation (Scopus)
24 Downloads (Pure)


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 - 24th Iberoamerican Congress, CIARP 2019, Proceedings
EditorsIngela Nyström, Yanio Hernández Heredia, Vladimir Milián Núñez
Number of pages11
ISBN (Electronic)978-3-030-33904-3
ISBN (Print)978-3-030-33903-6
Publication statusPublished - Oct-2019
Event24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 - Havana, Cuba
Duration: 28-Oct-201931-Oct-2019

Publication series

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


Conference24th Iberoamerican Congress on Pattern Recognition, CIARP 2019


  • Few shot training
  • Sound event detection
  • Trainable features

Cite this