@inproceedings{d930c9b36d734ae4bbd1d69ec9c70603,
title = "Towards the characterization of singing styles in world music",
abstract = "In this paper we focus on the characterization of singing styles in world music. We develop a set of contour features capturing pitch structure and melodic embellishments. Using these features we train a binary classifier to distinguish vocal from non-vocal contours and learn a dictionary of singing style elements. Each contour is mapped to the dictionary elements and each recording is summarized as the histogram of its contour mappings. We use K-means clustering on the recording representations as a proxy for singing style similarity. We observe clusters distinguished by characteristic uses of singing techniques such as vibrato and melisma. Recordings that are clustered together are often from neighbouring countries or exhibit aspects of language and cultural proximity. Studying singing particularities in this comparative manner can contribute to understanding the interaction and exchange between world music styles.",
keywords = "features, pitch, singing, unsupervised learning, world music",
author = "Maria Panteli and Rachel Bittner and Bello, {Juan Pablo} and Simon Dixon",
note = "Funding Information: This work was partially supported by the NYUAD Research Enhancement Grant # RE089 and the EPSRC-funded Platform Grant: Digital Music (EP/K009559/1). Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952233",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "636--640",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}