Combining Features Extracted from Audio, Symbolic and Cultural Sources

This paper experimentally investigates the classification utility of combining features extracted from separate audio, symbolic and cultural sources of musical information. This was done via a series of genre classification experiments performed using all seven possible combinations and subsets of the three corresponding types of features. These experiments were performed using jMIR, a software suite designed for use both as a toolset for performing MIR research and as a platform for developing and sharing new algorithms. The experimental results indicate that combining feature types can indeed substantively improve classification accuracy. Accuracies of 96.8% and 78.8% were attained respectively on 5 and 10-class genre taxonomies when all three feature types were combined, compared to average respective accuracies of 85.5% and 65.1% when features extracted from only one of the three sources of data were used. It was also found that combining feature types decreased the seriousness of those misclassifications that were made, on average, particularly when cultural features were included.