Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95168
Title: Music Emotion Classification: Analysis of a Classifier Ensemble Approach
Authors: Panda, Renato 
Paiva, Rui Pedro 
Issue Date: 2012
Project: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music 
Serial title, monograph or event: 5th International Workshop on Music and Machine Learning – MML 2012 – in conjunction with the 19th International Conference on Machine Learning – ICML 2012
Place of publication or event: Edinburgh, UK
Abstract: 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.
URI: http://hdl.handle.net/10316/95168
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

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