Music Genre Classification Using Stacked Auto-Encoders

Abstract

In this paper, we propose an architecture based on a stacked auto-encoder (SAE) for the classification of music genre. Each level in the stacked architecture works by stacking some hidden representations resulting from the previous level and related to different frames of the input signal. In this way, the proposed architecture shows a more robust classification compared to a standard SAE. The input to the first level of the SAE is fed by a set of 57 peculiar features extracted from the music signals. Some experimental results show the effectiveness of the proposed approach with respect to other state-of-the-art methods. In particular, the proposed architecture is compared to the support vector machine (SVM), multi-layer perceptron (MLP) and logistic regression (LR).