Issues in Automatic Musical Genre Classification

A novel software system that automatically classifies musical recordings based on genre is presented and discussed. This system is intended as a demonstration of how automated musical feature extraction from MIDI files, machine learning and pattern recognition techniques can be applied to the general tasks of music classification and grouping. The nebulous definitions and overlapping boundaries of genres makes reliable and consistent genre classification a difficult task for humans and computers alike. Traditional rules-based classification systems are severely limited by these factors as well as by the dynamic nature of genres. The techniques used in this software system are presented as alternative methods that can help to overcome these limitations. Arriving at a realistic and useful musical taxonomy can also be a difficult task. The problems associated with this task are briefly reviewed and some possible ways in which technology can be applied to improve the process of taxonomy construction are presented. The highlights of the catalogue of musical features that the software extracts from symbolic musical data are presented in the context of how the features can be used both for automatic classification and for statistical musicological studies. The easy to use and flexible interface of the software is also demonstrated as a resource that could easily be adapted to a variety of areas of musical research. Several automated pattern recognition and classification techniques are also briefly presented in order to demonstrate how they can be applied to musical research.