Computer Science ›› 2016, Vol. 43 ›› Issue (9): 310-314.doi: 10.11896/j.issn.1002-137X.2016.09.062

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Research on Background Model Adaptation for Acoustic Event Detection and Classification Based on Acoustic Surveillance System

ZHANG Ai-ying and NI Chong-jia   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Acoustic event detection and classification have become an important research problem as the increasing use of audio sensors in surveillance system.In these systems,audio circumstance is very complicated,that is,different locations,different noises,which cause the acoustic event detection and classification to be very difficult.Therefore,it is important to implement the background model adaptation in order to adapt these variations of background.In this paper,we proposed to use the constrained maximum likelihood linear regression (CMLLR) to adapt background model.Using the real world data and simulated data,we investigate the background model adaptation approaches and strategies for background model adaptation.Experimental results show that background model adaptation can improve the perfor-mance of target acoustic event detection and classification,and also can greatly reduce the false alarm of target acoustic event detection and classification.

Key words: Acoustic event detection and classification,Background model adaptation,Constrained maximum likelihood linear regression (CMLLR),Surveillance system

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