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Downbeat Detection with Conditional Random Fields and Deep Learned Features
- Simon Durand #1, Slim Essid #1
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Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
- Télécom ParisTech
- CNRS : UMR5141
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- References
- International Society for Music Information Retrieval (ISMIR), New York City, USA, August 2016, pp. 386-392
- Abstract
In this paper, we introduce a novel Conditional Random Field (CRF) system that detects the downbeat sequence of musical audio signals. Feature functions are computed
from four deep learned representations based on harmony,
rhythm, melody and bass content to take advantage of the
high-level and multi-faceted aspect of this task. Downbeats
being dynamic, the powerful CRF classification system allows us to combine our features with an adapted temporal
model in a fully data-driven fashion. Some meters being
under-represented in our training set, we show that data
augmentation enables a statistically significant improvement of the results by taking into account class imbalance.
An evaluation of different configurations of our system
on nine datasets shows its efficiency and potential over a
heuristic based approach and four downbeat tracking algo-
rithms.
- Keywords
- Category
- Paper in proceedings
- Research Area(s)
- Statistics/Applications
Computer Science/Machine Learning
Computer Science/Neural and Evolutionary Computing
Computer Science/Sound
Computer Science/Signal and Image Processing
Engineering Sciences/Signal and Image processing
Statistics/Machine Learning
- Identifier(s)
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Bibliographic key SD:ISMIR-16
- File(s)
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- Last update
- on january 19, 2017 by Simon Durand
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