Cross-collection evaluation for music classification tasks

Bogdanov, Dmitry; Porter, Alastair; Herrera Boyer, Perfecto, 1964-; Serra, Xavier

Cross-collection evaluation for music classification tasks

Llicència

Citació

Bogdanov D, Porter A, Herrera P, Serra X. Cross-collection evaluation for music classification tasks. In: Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 379-85.

Resum

Many studies in music classification are concerned with obtaining the highest possible cross-validation result. However, some studies have noted that cross-validation may be prone to biases and that additional evaluations based on independent out-of-sample data are desirable. In this paper we present a methodology and software tools for cross-collection evaluation for music classification tasks. The tools allow users to conduct large-scale evaluations of classifier models trained within the AcousticBrainz platform, given an independent source of ground-truth annotations, and its mapping with the classes used for model training. To demonstrate the application of this methodology we evaluate five models trained on genre datasets commonly used by researchers for genre classification, and use collaborative tags from Last.fm as an independent source of ground truth. We study a number of evaluation strategies using our tools on validation sets from 240,000 to 1,740,000 music recordings and discuss the results.

Descripció

Comunicació presentada a la 17th International Society for Music Information Retrieval Conference (ISMIR 2016), celebrada els dies 7 a 11 d'agost de 2016 a Nova York, EUA.