Audio surveillance using a bag of aural words classifier

Vincenzo Carletti, Pasquale Foggia, Gennaro Percannella, Alessia Saggese, Nicola Strisciuglio, Mario Vento

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

30 Citations (Scopus)

Abstract

In this paper we propose a novel approach for the audio-based detection of events. The approach adopts the bag of words paradigm, and has two main advantages over other techniques present in the literature: the ability to automatically adapt (through a learning phase) to both short, impulsive sounds and long, sustained ones, and the ability to work in noisy environments where the sounds of interest are superimposed to background sounds possibly having similar characteristics. The proposed method has been experimentally validated on a large database of sounds, including several kinds of background noise, which are superimposed to the sounds to be recognized. The obtained performance has been compared with the results of another audio event detection algorithm from the literature, showing a significant improvement.

Original languageEnglish
Title of host publication2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Place of PublicationPiscataway, NJ
PublisherIEEE Computer Society
Pages81-86
Number of pages6
ISBN (Print)978-1-4799-0703-8
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 - Krakow, Poland
Duration: 27 Aug 201330 Aug 2013
Conference number: 10

Conference

Conference2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Abbreviated titleAVSS 2013
CountryPoland
CityKrakow
Period27/08/1330/08/13

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  • Cite this

    Carletti, V., Foggia, P., Percannella, G., Saggese, A., Strisciuglio, N., & Vento, M. (2013). Audio surveillance using a bag of aural words classifier. In 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 (pp. 81-86). [6636620] Piscataway, NJ: IEEE Computer Society. https://doi.org/10.1109/AVSS.2013.6636620