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Supporting folk-song research by automatic metric learning and ranking (2009)
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| Venue: | In Proc. of the 10th Int. Conf. on Music Information Retrieval (ISMIR’09 |
| Citations: | 8 - 5 self |
BibTeX
@INPROCEEDINGS{Bade09supportingfolk-song,
author = {Korinna Bade and Andreas Nürnberger and Sebastian Stober and Jörg Garbers and Frans Wiering},
title = {Supporting folk-song research by automatic metric learning and ranking},
booktitle = {In Proc. of the 10th Int. Conf. on Music Information Retrieval (ISMIR’09},
year = {2009},
pages = {741--746}
}
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Abstract
In folk song research, appropriate similarity measures can be of great help, e.g. for classification of new tunes. Several measures have been developed so far. However, a particular musicological way of classifying songs is usually not directly reflected by just a single one of these measures. We show how a weighted linear combination of different basic similarity measures can be automatically adapted to a specific retrieval task by learning this metric based on a special type of constraints. Further, we describe how these constraints are derived from information provided by experts. In experiments on a folk song database, we show that the proposed approach outperforms the underlying basic similarity measures and study the effect of different levels of adaptation on the performance of the retrieval system. 1.
Keyphrases
automatic metric learning folk-song research folk song research several measure specific retrieval task folk song database different basic similarity measure different level special type particular musicological way underlying basic similarity measure appropriate similarity measure new tune weighted linear combination great help retrieval system
