Hierarchical Classification of Musical Instruments on Solo Recordings
Slim Essid, Gaël Richard, Bertrand David · 2006
We propose a study on the use of hierarchical taxonomies for musical instrument recognition on solo recordings. Both a natural taxonomy (inspired by instrument families) and a taxonomy inferred automatically by means of hierarchical clustering are examined. They are used to build a hierarchical classification scheme based on support vector machine classifiers and an efficient selection of features from a wide set of candidate descriptors. The classification results found with each taxonomy are compared and analysed. The automatic taxonomy is found to perform slightly better than the "natural" one. However, our analysis of the confusion matrices related to these taxonomies suggest that both are limited. In fact, it shows that it could be more advantageous to utilise taxonomies such that the instruments which are commonly confused are put in distinct decision nodes