Music Emotion Classification: Analysis of a Classifier Ensemble Approach
Renato Panda, Rui Pedro Paiva · Estudo Geral (Universidade de Coimbra) · 2012
We propose a five regression models’ system to classify music emotion. To this end, a dataset similar to MIREX contest dataset was used. Songs from each cluster are separated in five sets and labeled as 1. A similar number of songs from other clusters are then added to each set and labeled 0, training regression models to output a value representing how much a song is related to the specific cluster. The five outputs are combined and the highest score used as classification. An F-measure of 68.9% was obtained. Results were validated with 10-fold cross-validation and feature selection was tested.