Time-frequency analysis for audio event detection in real scenarios

Alessia Saggese, Nicola Strisciuglio, Mario Vento, Nicolai Petkov

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

16 Citations (Scopus)
4 Downloads (Pure)

Abstract

We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short-and long-Time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of sounds. Similarly to the words in a text, we count the occurrence of audio units for the construction of a feature vector that describes a given time interval. A classifier is then used to learn which audio units are distinctive for the different classes of sound. We compare the performance of different sets of short-Time features by carrying out experiments on the MIVIA audio event data set. We study the performance and the stability of the proposed system when it is employed in live scenarios, so as to characterize its expected behavior when used in real applications.

Original languageEnglish
Title of host publication2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages438-443
Number of pages6
ISBN (Electronic)9781509038114
DOIs
Publication statusPublished - 7-Nov-2016
Event13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016 - Colorado Springs, United States
Duration: 23-Aug-201626-Aug-2016

Conference

Conference13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016
Country/TerritoryUnited States
CityColorado Springs
Period23/08/201626/08/2016

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