2005:Audio Onset Detect - Revision history

Admin at 16:47, 15 November 2005

2005-11-15T16:47:25Z

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Tim1 at 22:42, 5 October 2005

2005-10-05T22:42:22Z

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It2wiki at 22:25, 5 October 2005

2005-10-05T22:25:09Z

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Tim1 at 20:54, 5 October 2005

2005-10-05T20:54:35Z

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It2wiki at 18:42, 5 October 2005

2005-10-05T18:42:42Z

It2wiki at 07:06, 4 October 2005

2005-10-04T07:06:42Z

SpamCleaner at 20:51, 28 September 2005

2005-09-28T20:51:37Z

Tim at 10:23, 27 September 2005

2005-09-27T10:23:10Z

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It2wiki at 22:42, 21 September 2005

2005-09-21T22:42:27Z

It2wiki at 21:00, 19 September 2005

2005-09-19T21:00:22Z

← Older revision		Revision as of 18:42, 5 October 2005
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	'' M = 100 * Om / Ofn ''		'' M = 100 * Om / Ofn ''
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← Older revision		Revision as of 07:06, 4 October 2005
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@@ Line 1: / Line 1: @@
-The aim of this contest is to compare state-of-the-art onset detection algorithms on music recordings. The methods will be evaluated on a large, various and reliably-annotated dataset, composed of sub-datasets grouping files of the same type.
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-''Audio content'':
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-The dataset is subdivided into classes, because onset detection is sometimes performed in applications dedicated to a single type of signal (ex: segmentation of a single track in a mix, drum transcription, complex mixes databases segmentation...). The performance of each algorithm will be assessed on the whole dataset but also on each class separately.
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-==Review 1==
+==Description==
-Besides being useful per se, onset detection is a pre-processing step for further music processing: rhythm analysis, beat tracking, instrument classification, and so on. It would be interesting that the proposal shortly discusses whether the evaluation metrics are unbiased wrt to the different potential applications.
+The aim of this contest is to compare state-of-the-art onset detection algorithms on music recordings. The methods will be evaluated on a large, various and reliably-annotated dataset, composed of sub-datasets grouping files of the same type.
-In order to decide which algorithm is the winner a single number should be finally extracted. A possibility to do so is tuning the algorithms to a single working point on the ROC curve, e.g. say allow a difference between FP and FN of less than 1%.
+) '''Input data'''
-It does not mention whether there will be training data available to participants.
+''Audio format'':
-I cannot tell whether the suggested participants are willing to participate. Other potential candidates could be: Simon Dixon, Harvey Thornburg, Masataka Goto.
+The data are monophonic sound files, with the associated onset times and

← Older revision	Revision as of 20:51, 28 September 2005
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