Model-based cover song detection via threshold autoregressive forecasts

Comunicació presentada a ACM Multimedia, International Workshop on Machine Learning and Music (MML), celebrat del 25 al 29 d'octubre de 2010 a Florència, Itàlia. Current systems for cover song detection are based on a model-free approach: they basically search for similarities in descriptor time series reflecting the evolution of tonal information in a musical piece. In this contribution we propose the use of a model-based approach. In particular, we explore threshold autoregressive models and the concept of cross-prediction error, i.e. a measure of to which extent a model trained on one song's descriptor time series is able to predict the covers'. Results indicate that the considered approach can provide competitive accuracies while being considerably fast and with potentially less storage requirements. Furthermore, the approach is parameter-free from the user's perspective, what provides a robust and straightforward application of it. We thank Emilia G´omez for her review on a previous version of this article. J.S. has been partially funded by the A/09/96235 grant from the Deutscher Akademisch Austausch Dienst and by the Music 3.0 (TSI-070100- 2008-318) and Buscamedia (CEN-20091026) projects. R.G.A. has been funded by the BFU2007-61710 grant of the Spanish Ministry of Education and Science.