Scream and gunshot detection and localization for audio surveillance systems
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Scream and Gunshot Detection and Localization for Audio-Surveillance Systems. G. Valenzise * , L. Gerosa, M. Tagliasacchi * , F. Antonacci * , A. Sarti *. * Dipartimento di Elettronica e Informazione, Politecnico di Milano. IEEE Int. Conf. On Advanced Video and Signal-based Surveillance, 2007.

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Scream and gunshot detection and localization for audio surveillance systems

Scream and Gunshot Detection and Localization for Audio-Surveillance Systems

G. Valenzise*, L. Gerosa, M. Tagliasacchi*, F. Antonacci*, A. Sarti*

*Dipartimento di Elettronica e Informazione,

Politecnico di Milano

IEEE Int. Conf. On Advanced Video and Signal-based Surveillance, 2007


Presentation outline
Presentation Audio-Surveillance SystemsOutline

  • Descriptionof the problem

  • System Overview

  • Classification

    • GMM

    • Featureextraction

    • Featureselection

    • Experimentalresults

  • Localization

    • TimeDelayEstimation

    • Source Localization

    • Experimentalresults


Description of the problem
Description Audio-Surveillance Systemsof the problem

  • Increasingneedforsafety in public places (e.g. squares):

    • High degreeofcriminality

    • Largenumberofvideo-camerasinstalled

       Aidto the humancontrolof the video-surveillancesystemsusingaudio signaltodetect and localizeanomalousevents (e.g. gunshots, screams) and tosteer a video-camera


General classification of events
General Audio-Surveillance SystemsClassificationofevents


Feature extraction
Feature Audio-Surveillance SystemsExtraction


Correlation features example
Correlation Audio-Surveillance SystemsFeatures: example

Autocorrelation filtered in the frequency range 1000-2500 Hz


Feature selection
Feature Audio-Surveillance SystemsSelection

  • From the full set offeatures, wewant a vectoroflfeatures:

    • Similardiscriminationpower

    • Lesscomputationally intensive

    • Resistanttooverfitting

Filter-based

featurevector

construction

Wrapper-based

featurevector

selection


Feature selection example
Feature Audio-Surveillance SystemsSelection: example


Experimental results classification at different snrs
Experimental Audio-Surveillance Systemsresults: classification at differentSNRs

Test: 0dB

Test: 5dB

Test: 15dB

Test: 10dB

Test: 20dB


Localization setup
Localization Audio-Surveillance Systems: setup

  • Consider a T-shaped mic array

  • Center mic is taken as reference

  • Localization problem can be split in two tasks:

    • Estimate Time Differences of Arrivals (TDOA) between each mic and reference mic

    • Estimate source location from TDOAs


Step Audio-Surveillance Systems 1: TimeDelayEstimation

  • Acousticmodelof the audio signalreceived at a coupleofmicrophones:

  • The TDE problemconsists in the estimationofτ GCC

Generalized Cross Correlation (GCC)

signal waveform


Step 2 source localization
Step Audio-Surveillance Systems 2: source localization

Linear-CorrectionLeastSquaresLocalization

(Huang & Benesty, 2004)


Experimental results localization threshold effect
Experimental Audio-Surveillance Systemsresults: Localization – Thresholdeffect

  • SNR > threshold small TDOA estimation errors around the true time delay

  • SNR < threshold  large errors on TDOA estimation


Experimental results localization angular error
Experimental Audio-Surveillance Systemsresults: Localization – AngularError


Conclusions future works
Conclusions Audio-Surveillance Systems & Future works

  • Combined system yields a precision of 93% and a false rejection rate of 5% at 10dB SNR

  • Hybrid feature selection allows to effectively select the most representative features with a reasonable computational effort

    Future Extensions:

  • Fusion of multiple mic arrays into a sensor network  increase range and precision


References
References Audio-Surveillance Systems

  • M. Figueiredo and A. Jain, “Unsupervised learning of finite mixture models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 381–396, 2002.

  • C. Knapp and G. Carter, “The generalized correlation method for estimation of time delay,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 24, no. 4, pp. 320–327, 1976.

  • J. Chen, Y. Huang, and J. Benesty, Audio Signal Processing for Next-Generation Multimedia Communication Systems. Kluwer, 2004, ch. 4-5

  • J. Ianniello, “Time delay estimation via cross-correlation in the presence of large estimation errors,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 30, no. 6, pp. 998–1003, 1982


Thank you
Thank Audio-Surveillance Systemsyou


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