@inproceedings{4a975f7a17c943a585040573bc09da03,
title = "Enhancing downbeat detection when facing different music styles",
abstract = "This paper focuses on the automatic rhythm analysis of musical audio at the bar level. We propose a novel approach for robust downbeat detection. It uses well-chosen complementary features, inspired by musical considerations. In particular, a note accentuation model and a detection of pattern changes are introduced. We estimate the time signature by examining the similarity of frames at the beat level. The features are selected through a linear SVM model or a weighted sum. The whole system is evaluated on five different datasets of various musical styles and shows improvement over the state of the art.",
keywords = "Downbeat-tracking, Music Information Retrieval, Music Signal Processing",
author = "Simon Durand and Bertrand David and Gael Richard",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICASSP.2014.6854177",
language = "English",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3132--3136",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}