Music Emotion Classification: Analysis of a Classifier Ensemble Approach

• Author(s): Panda, Renato ; Paiva, Rui Pedro
• Date: 2012
• Persistent ID: https://hdl.handle.net/10316/95168
• Origin: Estudo Geral - Universidade de Coimbra
• Project/scholarship: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT
• Document Type: Other
• Language: English

Description

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.

This work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e a Tecnologia (FCT) and Programa Operacional Temático Factores de Competitividade (COMPETE) - Portugal.