Abstract. We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5×5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates.

@article{anglade:mgc:2010,
Author = {Am\´{e}lie Anglade and Emmanouil Benetos and Matthias Mauch and Simon Dixon},
Booktitle = {Journal of New Music Research, accepted},
Title = {Improving Music Genre Classification Using Automatically Induced Harmony Rules},
Year = {2010}}