Music genre classification using principal component analysis and auto associative neural network

Abstract

The aim of music genre classification is to classify music pieces according to their style. Principal Component Analysis (PCA) is applied on raw music signals to capture the major components for each genre. As a large number of principal components are obtained for different cases, the purpose of applying PCA is not satisfied. This led to feature vector extraction from the music signal and building a model to capture the feature vector distribution of a music genre. Timbre modelling is done using Mel Frequency Cepstral Coefficients (MFCCs). The modelling of decision logic is based on Auto Associative Neural Network (AANN) models, which are feed-forward neural networks that perform identity mapping on the input space. The property of a five layer AANN model to capture the feature vector distribution is used to build a music genre classification system. This system is developed using a music database of 1000 songs spanning equally over 10 genres.