Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks

Sebastian Böck; Jan Schlüter; Gerhard Widmer

Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks

Sebastian Böck, Jan Schlüter, Gerhard Widmer

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

We present a new neural network based peak-picking algorithm for common onset detection functions. Compared to existing handcrafted methods it yields a better performance and leads to a much lower number of false negative detections. The performance is evaluated on basis of a huge dataset with over 25k annotated onsets and shows a significant improvement over existing methods in cases of signals with previously unknown levels.

Original language	English
Title of host publication	Proceedings of the 6th International Workshop on Machine Learning and Music, European Conference on Machine Learning (ECML 2013)
Number of pages	6
Publication status	Published - Sept 2013

Fields of science

102 Computer Sciences
102001 Artificial intelligence
102003 Image processing

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

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title = "Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks",

abstract = "We present a new neural network based peak-picking algorithm for common onset detection functions. Compared to existing handcrafted methods it yields a better performance and leads to a much lower number of false negative detections. The performance is evaluated on basis of a huge dataset with over 25k annotated onsets and shows a significant improvement over existing methods in cases of signals with previously unknown levels.",

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year = "2013",

month = sep,

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booktitle = "Proceedings of the 6th International Workshop on Machine Learning and Music, European Conference on Machine Learning (ECML 2013)",

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AU - Schlüter, Jan

AU - Widmer, Gerhard

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AB - We present a new neural network based peak-picking algorithm for common onset detection functions. Compared to existing handcrafted methods it yields a better performance and leads to a much lower number of false negative detections. The performance is evaluated on basis of a huge dataset with over 25k annotated onsets and shows a significant improvement over existing methods in cases of signals with previously unknown levels.

M3 - Conference proceedings

BT - Proceedings of the 6th International Workshop on Machine Learning and Music, European Conference on Machine Learning (ECML 2013)

ER -