Music mood classification using visual and acoustic features

Abstract

This work aims to present a system for automatic music mood classification based on acoustic and visual features extracted from the music. The visual features are obtained from spectrograms and the acoustic features are extracted directly from the audio signal. The texture operators used are Local Phase Quantization (LPQ), Local Binary Pattern (LBP) and Robust Local Binary Pattern (RLBP). The acoustic features are described using Rhythm Patterns (RP), Rhythm Histogram (RH) and Statistical Spectrum Descriptor (SSD). The experiments performed were made on a subset of the Latin Music Mood Database, considering three mood classes: positive, negative, and neutral. In the classification step, SVM classifier was used and the final results were taken by using 5-fold cross validation. From several classifiers created performing a zoning strategy along the images, the best individual classifier is that created from the image zone which corresponds to the frequencies from 1,700 Hz to 3,400 Hz, and using the RLBP visual descriptor. In this case, the F-measure obtained is about 59.55%.