Difference between revisions of "2005:Audio Onset Detect"

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==Review 1==
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==Description==
  
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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.
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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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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%.
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1) '''Input data'''
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The evaluation should account for a statistical significance measure. I suppose McNemar's test could do the job.
 
  
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It does not mention whether there will be training data available to participants.
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''Audio format'':
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To my understanding, evaluation on the following three subcategories is enough: monophonic instrument, polyphonic solo instrument and complex mixes.
 
  
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I cannot tell whether the suggested participants are willing to participate. Other potential candidates could be: Simon Dixon, Harvey Thornburg, Masataka Goto.
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The data are monophonic sound files, with the associated onset times and
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data about the annotation robustness.
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* CD-quality (PCM, 16-bit, 44100 Hz)
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* single channel (mono)
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* file length between 2 and 36 seconds (total time: 14 minutes)
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* File names:
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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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The dataset contains 85 files from 5 classes annotated as follows:
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* 30 solo drum excerpts cross-annotated by 3 people
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* 30 solo monophonic pitched instruments excerpts cross-annotated by 3 people
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* 10 solo polyphonic pitched instruments excerpts cross-annotated by 3 people
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* 15 complex mixes cross-annotated by 5 people
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Moreover the monophonic pitched instruments class is divided into 6 sub-classes: brass (2 excerpts), winds (4), sustained strings (6), plucked strings (9), bars and bells (4), singing voice (5).
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''Nomenclature''
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<AudioFileName>.wav for the audio file
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2) '''Output data'''
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The onset detection algoritms will return onset times in a text file: <Results of evaluated Algo path>/<AudioFileName>.output.
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''Onset file Format''
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<onset time(in seconds)>\n
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where \n denotes the end of line. The < and > characters are not included.

Revision as of 16:00, 19 September 2005

Description

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.

1) Input data

Audio format:

The data are monophonic sound files, with the associated onset times and data about the annotation robustness.

  • CD-quality (PCM, 16-bit, 44100 Hz)
  • single channel (mono)
  • file length between 2 and 36 seconds (total time: 14 minutes)
  • File names:

Audio content:

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.

The dataset contains 85 files from 5 classes annotated as follows:

  • 30 solo drum excerpts cross-annotated by 3 people
  • 30 solo monophonic pitched instruments excerpts cross-annotated by 3 people
  • 10 solo polyphonic pitched instruments excerpts cross-annotated by 3 people
  • 15 complex mixes cross-annotated by 5 people

Moreover the monophonic pitched instruments class is divided into 6 sub-classes: brass (2 excerpts), winds (4), sustained strings (6), plucked strings (9), bars and bells (4), singing voice (5).

Nomenclature

<AudioFileName>.wav for the audio file


2) Output data

The onset detection algoritms will return onset times in a text file: <Results of evaluated Algo path>/<AudioFileName>.output.


Onset file Format

<onset time(in seconds)>\n

where \n denotes the end of line. The < and > characters are not included.

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