A Comparison between Global and Local Features for Computational Classification of Folk Song Melodies

Abstract

In computational approaches to the study of variation among folk song melodies from oral culture, both global and local features of melodies have been used. From a computational point of view, the representation of a melody as a vector of global feature values, each summarizing an aspect of the entire melody, is attractive. However, from an annotation study on perceived melodic similarity and human categorization in music it followed that local features of melodies are most important to classify and recognize melodies. We compare both approaches in a computational classification task. In both cases, the discriminative power of features is assessed. We use a feature evaluation criterion that is based on the performance of a nearest-neighbour classifier. As distance measure for vectors of global features, we use the Euclidian distance. For the sequences of local features, we use the score of the Needleman–Wunsch alignment algorithm. In each of our comparisons, the local features correspond to the global features. In all cases, it appears that the local approach outperforms the global approach in a classification task for melodies, which indicates that local features carry more information about the identity of melodies. Therefore, locality is a crucial factor in modelling melodic similarity among folk song melodies.

Acknowledgements

This work was funded by the Netherlands Organisation for Scientific Research. It was carried out within the WITCHCRAFT project (NWO 640-003-501), which is part of the Continuous Access to Cultural Heritage (CATCH) program, and in the Tunes & Tales project, which is part of the Computational Humanities program of the Royal Netherlands Academy of Arts and Sciences. Anja Volk is supported by the Netherlands Organisation for Scientific Research, NWO-VIDI grant 276-35-001. We thank Marcelo E. Rodriguez López (Utrecht University) for careful proof reading and suggestions.

The Annotated Corpus can be obtained from the authors. The implementation of the alignment algorithm is available as C ++ -library from: http://libmusical. sourceforge.net.

Notes

¹What is the best method for the lexical ordering of folk and folklike tunes? (Translation by Nettl 2005, p. 123).

² http://www.liederenbank.nl

³ http://www.prtools.org (accessed 1 June 2011).

⁴We use the Matlab-implementation of PRTools (http://www.prtools.org, accessed 1 June 2011).