Bio-Inspired Filters for Audio Analysis

  • Nicola StrisciuglioEmail author
  • Mario Vento
  • Nicolai Petkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10087)


Nowadays, much is known about the functions of the components of the human auditory system. Computational models of these components are widely accepted and recently inspired the work of researchers in pattern recognition and signal processing. In this work we present a novel filter, which we call COPE (Combination of Peaks of Energy), that is inspired by the way the sound waves are converted into neuronal firing activity on the auditory nerve. A COPE filter creates a model of the pattern of the neural activity generated by a sound of interest and is able to detect the same pattern and modified versions of it. We apply the proposed method on the task of event detection for surveillance of roads. For the experiments, we use a publicly available data set, namely the MIVIA road events data set. The results that we achieve (recognition rate equal to \(94\%\) and false positive rate lower than \(4\%\)) and the comparison with existing methods demonstrate the effectiveness of the proposed bio-inspired filters for audio analysis.


Audio analysis Auditory system Bio-inspired filters Event detection Trainable COPE filters 


  1. 1.
    Azzopardi, G., Petkov, N.: A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biol. Cybern. 106(3), 177–189 (2012)CrossRefGoogle Scholar
  2. 2.
    Azzopardi, G., Petkov, N.: Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 490–503 (2013)CrossRefGoogle Scholar
  3. 3.
    Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)CrossRefGoogle Scholar
  4. 4.
    Blauert, J.: The Technology of Binaural Listening. Modern Acoustics and Signal Processing (2013)Google Scholar
  5. 5.
    Cano, P., Batlle, E., Kalker, T., Haitsma, J.: A review of audio fingerprinting. J. VLSI Sig. Process. Syst. Sig. Image Video Technol. 41(3), 271–284 (2005)CrossRefGoogle Scholar
  6. 6.
    Carletti, V., Foggia, P., Percannella, G., Saggese, A., Strisciuglio, N., Vento, M.: Audio surveillance using a bag of aural words classifier. In: IEEE AVSS, pp. 81–86, August 2013Google Scholar
  7. 7.
    Chin, M., Burred, J.: Audio event detection based on layered symbolic sequence representations. In: IEEE ICASSP, pp. 1953–1956 (2012)Google Scholar
  8. 8.
    Clavel, C., Ehrette, T., Richard, G.: Events detection for an audio-based surveillance system. In: ICME, pp. 1306–1309 (2005)Google Scholar
  9. 9.
    Conte, D., Foggia, P., Percannella, G., Saggese, A., Vento, M.: An ensemble of rejecting classifiers for anomaly detection of audio events. In: IEEE AVSS, pp. 76–81, September 2012Google Scholar
  10. 10.
    Crocco, M., Cristani, M., Trucco, A., Murino, V.: Audio surveillance: a systematic review. CoRR abs/1409.7787 (2014)Google Scholar
  11. 11.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  12. 12.
    Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., Vento, M.: Audio surveillance of roads: a system for detecting anomalous sounds. IEEE Trans. Intell. Transp. Syst. PP(99), 1–10 (2015)Google Scholar
  13. 13.
    Foggia, P., Saggese, A., Strisciuglio, N., Vento, M.: Cascade classifiers trained on gammatonegrams for reliably detecting audio events. In: IEEE AVSS, pp. 50–55, August 2014Google Scholar
  14. 14.
    Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., Vento, M.: Reliable detection of audio events in highly noisy environments. Pattern Recogn. Lett. 65, 22–28 (2015)CrossRefGoogle Scholar
  15. 15.
    Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–6(6), 721–741 (1984)CrossRefzbMATHGoogle Scholar
  16. 16.
    Jeffress, L.A.: A place theory of sound localization. J. Comp. Physiol. Psychol. 41(1), 35–39 (1948)CrossRefGoogle Scholar
  17. 17.
    Lecomte, S., Lengelle, R., Richard, C., Capman, F., Ravera, B.: Abnormal events detection using unsupervised one-class svm - application to audio surveillance and evaluation. In: IEEE AVSS, pp. 124–129, 30 2011-September 2 2011Google Scholar
  18. 18.
    Lopez-Poveda, E.A., Eustaquio-Martín, A.: A biophysical model of the inner hair cell: The contribution of potassium currents to peripheral auditory compression. J. Assoc. Res. Otolaryngol. 7(3), 218–235 (2006). Google Scholar
  19. 19.
    Meddis, R.: Auditory-nerve first-spike latency and auditory absolute threshold: a computer model. J. Acoust. Soc. Am. 119(1), 406–417 (2006)CrossRefGoogle Scholar
  20. 20.
    Ntalampiras, S., Potamitis, I., Fakotakis, N.: An adaptive framework for acoustic monitoring of potential hazards. EURASIP J. Audio Speech Music Process. 2009, 13:1–13:15 (2009)Google Scholar
  21. 21.
    Ogle, J.P., Ellis, D.P.W.: Fingerprinting to identify repeated sound events in long-duration personal audio recordings. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, ICASSP 2007, vol. 1, pp. I-233–I-236, April 2007Google Scholar
  22. 22.
    Palmer, A., Russell, I.: Phase-locking in the cochlear nerve of the guinea-pig and its relation to the receptor potential of inner hair-cells. Hear. Res. 24(1), 1–15 (1986)CrossRefGoogle Scholar
  23. 23.
    Patterson, R.D., Moore, B.C.J.: Auditory filters and excitation patterns as representations of frequency resolution. Frequency selectivity in hearing, pp. 123–177 (1986)Google Scholar
  24. 24.
    Patterson, R.D., Robinson, K., Holdsworth, J., Mckeown, D., Zhang, C., Allerhand, M.: Complex Sounds and auditory images. In: Cazals, Y., Demany, L., Honer, K. (eds.) Auditory Physiology and Perception, Pergamon, Pergamon, Oxford, pp. 429–443 (1992)Google Scholar
  25. 25.
    Phan, H., Hertel, L., Maass, M., Mazur, R., Mertins, A.: Audio phrases for audio event recognition. In: 23nd European Signal Processing Conference, EUSIPCO 2015 (2015)Google Scholar
  26. 26.
    Pour, A.F., Asgari, M., Hasanabadi, M.R.: Gammatonegram based speaker identification. In: 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE), pp. 52–55, October 2014Google Scholar
  27. 27.
    Poveda, E.A.L., Meddis, R.: A human nonlinear cochlear filterbank. J. Acoust. Soc. Am. 110(6), 3107–18 (2001)CrossRefGoogle Scholar
  28. 28.
    Rabaoui, A., Davy, M., Rossignol, S., Ellouze, N.: Using one-class svms and wavelets for audio surveillance. IEEE Trans. Inf. Forensics Security 3(4), 763–775 (2008)CrossRefGoogle Scholar
  29. 29.
    Strisciuglio, N., Azzopardi, G., Vento, M., Petkov, N.: Multiscale blood vessel delineation using B-COSFIRE filters. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 300–312. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-23117-4_26 CrossRefGoogle Scholar
  30. 30.
    Strisciuglio, N., Azzopardi, G., Vento, M., Petkov, N.: Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Mach. Vis. Appl., 1–13 (2016). doi: 10.1007/s00138-016-0781-7 Google Scholar
  31. 31.
    Sturm, B.L.: A survey of evaluation in music genre recognition. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds.) AMR 2012. LNCS, vol. 8382, pp. 29–66. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-12093-5_2 Google Scholar
  32. 32.
    Vacher, M., Istrate, D., Besacier, L., Serignat, J.F., Castelli, E.: Sound detection and classification for medical telesurvey. In: ACTA Press (eds.) Proceedings of the 2nd ICBME, Innsbruck, Austria, pp. 395–398, February 2004Google Scholar
  33. 33.
    Valenzise, G., Gerosa, L., Tagliasacchi, M., Antonacci, F., Sarti, A.: Scream and gunshot detection and localization for audio-surveillance systems. In: IEEE AVSS, pp. 21–26 (2007)Google Scholar
  34. 34.
    Wang, A.L.-C., Th Floor Block F.: An industrial-strength audio search algorithm. In: Proceedings of the 4th International Conference on Music Information Retrieval (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nicola Strisciuglio
    • 1
    • 2
    Email author
  • Mario Vento
    • 2
  • Nicolai Petkov
    • 1
  1. 1.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenGroningenThe Netherlands
  2. 2.Department of Information and Electrical Engineering and Applied MathematicsUniversity of SalernoFiscianoItaly

Personalised recommendations