Construction of bottle-body autoencoder and its application to audio signal classification

Abstract

In order to extract effective audio feature using autoencoder, different from traditional bottle-neck autoencoder, bottle-body autoencoder is presented in this paper, which is constructed using restricted Boltzmann machine with the same neurons at every layer. Bottle-body feature, which is obtained by using pseudo-inverse method to initialize weights, is applied to audio signal classification. The proposed approach is evaluated on the BBC Sound Effects Library, and shows a 14.90% and 16.20% improvement on classification accuracy than traditional Mel-frequency cepstral coefficient and bottle-neck feature.