Cascade classifiers trained on gammatonegrams for reliably detecting audio events

Pasquale Foggia*, Alessia Saggese, Nicola Strisciuglio, Mario Vento

*Corresponding author for this work

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

16 Citations (Scopus)

Abstract

In this paper we propose a novel method for the detection of events of interest through audio analysis. The system that we propose is based on the representation of the audio streams through a Gammatone image, which describes the time-frequency distribution of the energy of the signal; this representation is inspired by the functioning of the human auditory system. A pool of AdaBoost cascade classifiers, one for each class of events of interest, is involved in the event detection stage. The performance of the proposed system has been evaluated on a large data set of audio events for surveillance applications and the achieved results, compared with two state of the art approaches, confirm its effectiveness.

Original languageEnglish
Title of host publication11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages50-55
Number of pages6
ISBN (Electronic)978-1-4799-4871-0
ISBN (Print)978-1-4799-4870-3
DOIs
Publication statusPublished - 8 Oct 2014
Externally publishedYes
Event11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014 - Seoul, Korea, Republic of
Duration: 26 Aug 201429 Aug 2014
Conference number: 11

Conference

Conference11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2014
Abbreviated titleAVSS
Country/TerritoryKorea, Republic of
CitySeoul
Period26/08/1429/08/14

Fingerprint

Dive into the research topics of 'Cascade classifiers trained on gammatonegrams for reliably detecting audio events'. Together they form a unique fingerprint.

Cite this