Hierarchical taxonomies of classes arise in the analysis of many types of musical information, including genre, as a means of organizing overlapping categories at varying levels of generality. However, incorporating hierarchical structure into conventional machine learning systems presents a challenge: the use of independent binary classifiers for each class in the hierarchy can produce hierarchically inconsistent predictions. That is, an example may be assigned to a class, and not assigned to the parent of that class. This paper applies a Bayesian framework to combine, or aggregate, a hierarchy of multiple binary classifiers in a principled manner, and consequently improves performance over the hierarchy as a whole. Furthermore, such an approach allows for an arbitrarily complex hierarchy, and does not suffer from classes that are too broad or too refined. Experiments on the MIREX 2005 symbolic genre classification dataset show that our Bayesian Aggregation algorithm provides significant improvement over independent classifiers, and demonstrates superior performance compared to previous work. Our method also improves similarity search by ranking songs by similarity of hierarchical predictions to those of a query song.

@inproceedings{DeCoro:2007:BAF,
   author = "Christopher DeCoro and Zafer Barutcuoglu and Rebecca Fiebrink",
   title = "{Bayes}ian Aggregation for Hierarchical Genre Classification",
   booktitle = "International Symposium on Music Information Retrieval 2007",
   year = "2007",
   month = sep
}