A neural network based audio content classification

The emergence of digital music in the Internet calls for a reliable real-time tool to analyze and properly categorize them for the users. To incorporate content or genre queries in web searches, audio content analysis and classification is imperative. This paper proposes a set of audio content features and a parallel Neural Network architecture that addresses the task of automated content based audio classification. Feature sets based on signal periodicity, beat information, sub-band energy, Mel-frequency Cepstral coefficients and Wavelet transforms are proposed and each of the feature sets are individually analyzed for their pertinence in the proposed task. A parallel Multi-layered Perceptron network is proposed which offers a classification accuracy of 84.4% to distinguish between 6 different genres. The proposed architecture is compared with a Support Vector Machine based classifier and is found to perform superiorly than the later.