Graph-based multi-sensor fusion for acoustic signal classification

Umamahesh Srinivas, Nasser M. Nasrabadi, Vishal Monga

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Scopus citations

    Abstract

    Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e. rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as recently proposed joint sparsity models for multi-sensor acoustic signal classification.

    Original languageEnglish (US)
    Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
    Pages261-265
    Number of pages5
    DOIs
    StatePublished - Oct 18 2013
    Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
    Duration: May 26 2013May 31 2013

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
    Country/TerritoryCanada
    CityVancouver, BC
    Period5/26/135/31/13

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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