Trainable wavelet-like transform for feature extraction to audio classification

Abstract

A method of learning optimal orthonormal filters for feature extraction from 1-D signal based on learning wavelet-like transform was proposed. Filters had been learned by using backpropagation simultaneously with neural network, which was used as a classifier. Orthonormality of filters during the learning process was provided by several quadratic regularization terms that follow from the orthogonality of the scaling functions. The proposed method was evaluated on the environmental sound classification task. We used the trainable wavelet-like transform and wavelet transform with different bases as feature extraction methods with fixed architecture of the neural network. The proposed method obtained the best results. The spectrum characteristics of learned filters of wavelet-like transform were compared with the corresponding characteristics of reverse biorthogonal wavelet basis rbior1.5 that obtained the closest accuracy results.