Neural Network Optimization by Genetic Algorithms for the Audio Classification to Speech and Music
In this paper, the execution of some features based on wavelet transform are evaluated through classification of audio to speech and music using the MLP (Multi Layer Perceptron) classifiers optimized by genetic algorithm. Classification results show the wavelet features are completely successful in speech/music classification. Experimental comparisons using different wavelets are presented and discussed. By using some wavelet features, extracted from 1-second segments of the signal, the authors obtained 96.49% accuracy in the audio classification of the MLP classifiers optimized by genetic algorithm.