Performance Evaluation of a Generalized Music Mood Classification Model

摘要

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is a new feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. Our experiments show that the feature reduction method suggested in this paper has better performance than that of the conventional dimension reduction methods, R-Squared and PCA. As performance improvement by feature reduction only is limited, the modular neural network approach is used additionally. The experiments also show that the method improves the performance effectively.