Presenting a Method for Classifying Audio Signals Based on MLP Neural Network

Classifying audio signals to groups such as music and speech is significant for many multimedia document recovery systems. So far, several approaches have been proposed for categorizing audio data as music or speech. In this paper, two approaches have been taken for discriminating and classifying these data; in one of them audio signals have a binary classification of speech/music and in another there is a ternary classification of speech/music/mixture. After extracting some audio features (such as low short-time energy ratio of frames, standard deviation of the zero-crossing rate, mean and variance of the spectral flux, mean and variance of the discrete wavelet transform, mean of the linear predictive coefficients, mean of the Mel frequency cepstral coefficients), the MLP neural network was used to classify them. Simulations on the network showed a classification accuracy of 98.7% for the binary classification and 93.3% for the ternary one. This confirms the usefulness of the proposed method.