Probabilistic Combination of Features for Music Classification.

We describe an approach to the combination of music similarity feature spaces in the context of music classification. The approach is based on taking the product of posterior probabilities obtained from separate classifiers for the different feature spaces. This allows for a different influence of the classifiers per song and an overall classification accuracy improving those resulting from individual feature spaces alone. This is demonstrated by combining spectral and rhythmic similarity for classification of ballroom dance music.