2007:Audio Onset Detection
Originally proposed (2005) by Paul Brossier and Pierre Leveau . Has run in 2005 and 2006.
- Dan Stowell (Queen Mary)
- Alexandre Lacoste (Montr├⌐al)
- Axel Roebel (Paris)
- Ruohua Zhou (queen mary / epfl)
- Wanchi Lee (Los Angeles)
- The text of this section is largely copied from the 2006 page
The onset detection contest is a continuation of the 2005/2006 Onset Detection contest.
essentially the same as 2005/2006
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)
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
The onset detection algorithms 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.
A README file accompanying each submission should contain explicit instructions on how to to run the program. In particular, each command line to run should be specified, using %input% for the input sound file and %output% for the resulting text file.
For instance, to test the program foobar with different values for parameters param1 and param2, the README file would look like:
foobar -param1 .1 -param2 1 -i %input% -o %output% foobar -param1 .1 -param2 2 -i %input% -o %output% foobar -param1 .2 -param2 1 -i %input% -o %output% foobar -param1 .2 -param2 2 -i %input% -o %output% foobar -param1 .3 -param2 1 -i %input% -o %output% ...
For a submission using MATLAB, the README file could look like:
matlab -r "foobar(.1,1,'%input%','%output%');quit;" matlab -r "foobar(.1,2,'%input%','%output%');quit;" matlab -r "foobar(.2,1,'%input%','%output%');quit;" matlab -r "foobar(.2,2,'%input%','%output%');quit;" matlab -r "foobar(.3,1,'%input%','%output%');quit;" ...
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input%' and '%output%' strings.
- This text has been copied from the 2006 Onset detection page
The detected onset times will be compared with the ground-truth ones. For a given ground-truth onset time, if there is a detection in a tolerance time-window around it, it is considered as a correct detection (CD). If not, there is a false negative (FN). The detections outside all the tolerance windows are counted as false positives (FP). Doubled onsets (two detections for one ground-truth onset) and merged onsets (one detection for two ground-truth onsets) will be taken into account in the evaluation. Doubled onsets are a subset of the FP onsets, and merged onsets a subset of FN onsets.
- P = Ocd / (Ocd +Ofp)
- R = Ocd / (Ocd + Ofn)
- and the F-measure
- F = 2*P*R/(P+R)
with these notations:
- number of correctly detected onsets (CD)
- number of missed onsets (FN)
- number of merged onsets
- number of false positive onsets (FP)
- number of double onsets
Other indicative measurements:
- FP rate
- FP = 100. * (Ofp) / (Ocd+Ofp)
- Doubled Onset rate in FP
- D = 100 * Od / Ofp
- Merged Onset rate in FN
- M = 100 * Om / Ofn
Because files are cross-annotated, the mean Precision and Recall rates are defined by averaging Precision and Recall rates computed for each annotation.
To establish a ranking (and indicate a winner...), we will use the F-measure, widely used in string comparisons. This criterion is arbitrary, but gives an indication of performance. It must be remembered that onset detection is a preprocessing step, so the real cost of an error of each type (false positive or false negative) depends on the application following this task.
- percentage of correct detections / false positives (can also be expressed as precision/recall)
- time precision (tolerance from +/- 50 ms to less). For certain file, we can't be much more accurate than 50 ms because of the weak annotation precision. This must be taken into account.
- separate scoring for different instrument types (percussive, strings, winds, etc)
More detailed data:
- percentage of doubled detections
- speed measurements of the algorithms
- scalability to large files
- robustness to noise, loudness
Comments from participants
I (Dan) am happy to use the dataset as used in 2005/2006 - any comments/agreement/disagreement re that? Those approaches that use machine learning should presumably be trained on other data.
- Note: I found some problems with the dataset - a couple of the files are faulty (e.g. they're annotations of the wrong audio). At Queen Mary's we've been replacing those faulty files with new annotations, and we'd be happy to share the "fixed" dataset. I'd suggest that it's better to use accurate annotations, even though that sacrifices an element of comparability against the 05/06 results. (Still, it's only a small fraction of files that were at fault, so the results will be largely comparable.) --Danstowell 09:39, 23 February 2007 (CST)
Hi, Dan. Have you not been part of the previous evaluations? I wonder because in the previous experiments nobody had access to the original data. I don't have much insight behind the scenes, but the idea was that nobody would ever have the possibility to train on the real data. (Does this exclude people from Queen Mary who took part in the construction of the data tests??)
Now, obviously we have a problem if some of the data is wrong. So I guess you should contact the people that actually run the tests to send your corrected data. As I understand they took only part of the available datasets, so they should check whether they use these wrong annotations.
All of the data did come from Queen Mary, and we are aware of the faulty ground truths. There were only a few ones that were mislabeled, and I don't think it should have a large impact, as all files were actually validated against 3-5 annotations a piece. Some groups having source data is a reality we do have to live with though, as we depend on contributions from the community, especially in tasks like this where annotation is so labourious. Melody extraction's dataset, for instance, came exclusively from one participant as well. So we have to allow a certain amount of trust and integrity in such things. I think the fact that we do the parameter sweeping will hopefully even things, and any possible advantages, out in the end.