Abstract
Content based music genre classification is a key component for next generation multimedia search agents. This paper introduces an audio classification technique based on audio content analysis. Artificial Neural Networks (ANNs), specifically multi-layered perceptrons (MLPs) are implemented to perform the classification task. Windowed audio files of finite length are analyzed to generate multiple feature sets which are used as input vectors to a parallel neural architecture that performs the classification. This paper examines a combination of linear predictive coding (LPC), mel frequency cepstrum coefficients (MFCCs), Haar Wavelet, Daubechies Wavelet and Symlet coefficients as feature sets for the proposed audio classifier. Parallel to MLP, a Gaussian radial basis function (GRBF) based ANN is also implemented and analyzed. The obtained prediction accuracy of 87.3% in determining the audio genres claims the efficiency of the proposed architecture. The ANN prediction values are processed by a rule based inference engine (IE) that presents the final decision.
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References
Atal B and Schroeder M (1979). Predictive coding of speech signals and subjective error criteria. IEEE Trans Acoust Speech Signal Process 27(3): 247–254
Blum T, Keislar D, Wheaton J, Wold E (1999) Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information. U.S. Patent 5, 918, 223, (1999)
Graps A (1995). An introduction to wavelets, IEEE Computer Science and Engineering. IEEE Comput Soc 2(2): 50–61
Guo G and Li SZ (2003). Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14(1): 209–215
Kailath T (1974). A view of three decades of linear filtering theory. IEEE Trans Inf Theory 20(2): 146–181
Logan B (2000) Mel frequency cepstral coefficients for music modeling. In: Proceedings of the international symposium on music information retrieval (SMIR)
Markel JD and Gray A (1976). Linear prediction of speech. Communication & Cybernetics. Springer, Heidelberg
McGarry KJ, Wermter S, McIntyre J (1999) Knowledge extraction from radial basis function networks and multi-layer perceptrons. In: Proceedings of international joint conference on neural networks (IJCNN), Washinton, vol 4, pp 2494–2497
MPEG Requirement Group (1998) MPEG-7: overview of the MPEG-7 standard. ISO/IEC JTC1/SC29/WG11 N3752, France
National Communication System - Office Technology and Standards (1984) Federal Standard 1015, telecommunications: analog to digital conversion of radio voice by 2400 bit/second linear predictive coding
Principe JC, Euliano NR and Lefebvre WC (2000). Neural and adaptive systems: fundamentals through simulations. Wiley, New York
Rabiner L and Juang B (1993). Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs
Slaney M (1999) Auditory toolbox for Matlab, Interval Research Corporation, Version 2. http://cobweb.ecn.purdue.edu/~malcolm/interval/1998-010/AuditoryToolboxTechReport.pdf
Tzanetakis G, Essl G, Cook P (2001) Audio analysis using the discrete wavelet transform. In: Proceedings of WSES international conference, acoustics and music: theory and applications (AMTA), Skiathos, Greece
Wiener N (1949). Extrapolation, interpolation and smoothing of stationary time series with engineering applications. Technology Press/Wiley, New York
Wold H (1954). A study in the analysis of stationary time series, 2nd edn. Almquist and Wiksell, Stockholm
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Mitra, V., Wang, CJ. Content based audio classification: a neural network approach. Soft Comput 12, 639–646 (2008). https://doi.org/10.1007/s00500-007-0241-4
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DOI: https://doi.org/10.1007/s00500-007-0241-4