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Multiclass Support Vector Machines for Environmental Sounds Classification Using log-Gabor Filters

Authors: S. Souli, Z. Lachiri


In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.

To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.

Keywords: Environmental sounds, Log-Gabor filters, Spectrogram, SVM Multiclass, Visual features.

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[1] S. Chu, S. Narayanan, and C.C.J. Kuo, "Environmental Sound Recognition with Time-Frequency Audio Features,” IEEE Trans. on Speech, Audio, and Language Processing, vol. 17,no. 6 pp. 1142-1158, 2009.
[2] A. Rabaoui, M. Davy, S. Rossignol, and N. Ellouze. "Using One-Class SVMs and Wavelets for Audio Surveillance,” IEEE Transactions on Information Forensics and Security. Vol. 3, no.4, pp. 763-775, 2008.
[3] K. El-Maleh, A. Samouelian, and P. Kabal, "Frame-level noise classification in mobile environments,” in Proc. ICASSP, Phoenix, AZ, 1999, pp.237–240.
[4] G.Yu, and J.J. Slotine. "Audio Classification from Time-Frequency Texture,” In Proc. IEEE. ICASSP, Taipei, 2009, pp. 1677–1680.
[5] G. Yu, and J. J. Slotine, "Fast Wavelet-based Visual Classification,” in Proc. IEEE International Conference on Pattern Recognition ICPR, Tampa, 2008, pp.1-5.
[6] S. Souli, Z. Lachiri, "Environmental Sounds Classification Based on Visual Features,” CIARP, Springer, Chile, vol.7042, pp. 459-466, 2011.
[7] M. Kleinschmidt, "Methods for capturing spectro-temporal modulations in automatic speech recognition,” Electrical and Electronic Engineering Acoustics, Speech and Signal Processing Papers, Acta Acustica, vol.88, no. 3, pp.416-422, 2002.
[8] M. Kleinschmidt, "Localized spectro-temporal features for auto-matic speech recognition,” in Proc. Eurospeech, 2003, pp.2573-2576.
[9] L. He, M. Lech, N. Maddage, and, N. Allen,"Stress and Emotion Recognition Using Log-Gabor Filter,” Affective Computing and Intelligent Interaction and Workshops, ACII, 3rd International Conference on, Amsterdam, 2009, pp.1-6.
[10] T. Ezzat, J. Bouvrie, and T. Poggio, "Spectro-Temporal Analysis of Speech Using 2-D Gabor Filters,” Proc. Interspeech, Citeseer, 2007, pp. 1-4.
[11] T. Lamper, A. O’Keefe, S. E.M. "A survey of spectrogram track detection algorithms,” Applied Acoustics. vol. 71, pp. 87–100, 2010.
[12] Z. Xinyi, Y. Jianxiao, H. Qiang. "Research of STRAIGHT Spectrogram and Difference Subspace Algorithm for Speech Recognition,” Int. Congress On Image and Signal Processing (CISP), IEEE DOI Link , 2009, pp.1-4.
[13] L. He, M. Lech, N. C. Maddage and N. Allen. "Stress Detection Using Speech Spectrograms and Sigma-pi Neuron Units,” int. Conf. on Natural Computation, 2009, pp. 260-264.
[14] J. Dennis, and H.D.Tran, and H. Li. "Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions,” Signal Processing Letters, IEEE. vol. 18, pp. 130-133, 2011.
[15] G. Yu, S. Mallat and E. Bacry. "Audio Denoising by Time-Frequency Block Thresholding,” IEEE Transactions on Signal Processing, vol 56, pp. 1830-1839, 2008.
[16] N. Kwak, C. Choi, "Input Feature Selection for Classification Problems,” IEEE Trans, On Neural Networks, vol. 13, no.1, pp. 143-159, 2002.
[17] B. Scholkopf, and A. Smola, "Learning with Kernels,” MIT Press, 2001.
[18] The Leonardo Software website. (Online). Available: http: // Santa Monica, CA 90401.
[19] C.-W. Hsu, C-C. Chang, C-J. Lin, "A practical Guide to Support Vector Classification,” Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, 2009.