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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.

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Scream and Gunshot Detection and Localization for Audio-Surveillance Systems

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  1. 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

  2. PresentationOutline • Descriptionof the problem • System Overview • Classification • GMM • Featureextraction • Featureselection • Experimentalresults • Localization • TimeDelayEstimation • Source Localization • Experimentalresults

  3. Descriptionof 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

  4. GeneralClassificationofevents

  5. FeatureExtraction

  6. CorrelationFeatures: example Autocorrelation filtered in the frequency range 1000-2500 Hz

  7. FeatureSelection • From the full set offeatures, wewant a vectoroflfeatures: • Similardiscriminationpower • Lesscomputationally intensive • Resistanttooverfitting Filter-based featurevector construction Wrapper-based featurevector selection

  8. FeatureSelection: example

  9. Experimentalresults: classification at differentSNRs Test: 0dB Test: 5dB Test: 15dB Test: 10dB Test: 20dB

  10. Localization: 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

  11. Step 1: TimeDelayEstimation • Acousticmodelof the audio signalreceived at a coupleofmicrophones: • The TDE problemconsists in the estimationofτ GCC Generalized Cross Correlation (GCC) signal waveform

  12. Step 2: source localization Linear-CorrectionLeastSquaresLocalization (Huang & Benesty, 2004)

  13. Experimentalresults: Localization – Thresholdeffect • SNR > threshold small TDOA estimation errors around the true time delay • SNR < threshold  large errors on TDOA estimation

  14. Experimentalresults: Localization – AngularError

  15. Conclusions & 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

  16. References • 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

  17. Thankyou

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