Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Model-based cover song detection via threshold autoregressive forecasts
| Title | Model-based cover song detection via threshold autoregressive forecasts |
| Publication Type | Conference Paper |
| Year of Publication | 2010 |
| Conference Name | ACM Multimedia, Int. Workshop on Machine Learning and Music (MML) |
| Authors | Serrà, J. , Kantz H. , & Andrzejak R. G. |
| Pagination | 13-16 |
| Conference Start Date | 25/10/2010 |
| Publisher | ACM |
| Conference Location | Firenze, Italy |
| ISBN Number | 978-1-60558-933-6 |
| Abstract | 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. |
| preprint/postprint document | files/publications/mml0823-serra.pdf |
| Final publication | 10.1145/1878003.1878008 |
