Content based audio classification: a neural network approach

Authors:
Vikramjit Mitra;Chia-Jiu Wang
Affiliations:
University of Maryland, Department of Electrical and Computer Engineering, 20742, College Park, MD, USA;University of Colorado at Colorado Springs, Department of Electrical and Computer Engineering, 80933, Colorado Springs, CO, USA
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Year:
2008

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Dynamic and scalable audio classification by collective network of binary classifiers framework: An evolutionary approach

Neural Networks

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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.