JvC
| Domain | |
| Media | Audio |
| Size | |
| Instances | 150 |
| File Format | MIDI |
| Creation Date | |
| Task | Classification |
| Copyright | For research purpouses only. Not for commercial use. |
| URL | http://grfia.dlsi.ua.es/cm/projects/prosemus/database.php |
Description
Automatic music genre recognition, either from audio or symbolic sources, is a much researched topic in the music information retrieval community. When dealing with symbolic music sources like digital scores or MIDI files, the main melody of a music piece is often clearly identified. This corpus contains melodies from jazz and classical music pieces encoded as MIDI file tracks.
Quality
Each file is a Format 1 MIDI file that contains two tracks:
- The so called master track or track 'zero' where the following MIDI metaevents are optionally encoded:
- Track Name (often contains the song name)
- Time Signature
- Tempo
- Key Signature
- The melody track (track 'one') containing the melody line. This melody line it is not guaranted to be fully monophonic, althought it is so in most files.
Most files have the melody concatenated three times, and one or two silence bars at the beginning. Some melody tracks are polyphonic (double octaves, violin parts in a single track, etc...). If you desperately need the tracks to be monophonic, you can reduce them to monophonic tracks using the 'smf2txt/txt2smf toolchain' avalaible at the following address: http://grfia.dlsi.ua.es/gen.php?id=resources
A command line example that reduces all tracks of a MIDI file to monophonic tracks:
$ smf2txt -p 1 polyphonic.mid | txt2smf -f monophonic.mid
The '-p' option argument means whether you want to preserve the top (1) or bottom (2) line. Be aware, however, that the result may be not exactly what you would expect. A slight, perceptually not significant, overlapping of two otherwise consecutive notes could result in the second note being discarded.
Source
Pattern Recognition and Artificial Intelligence Group - University of Alicante (PRAIg-UA)
Ground Truth Annotation
Manually annotated. In Iñesta et al. (2008) this ground truth was used to asses human genre recognition capabilities in absence of timbre related information.
Evaluation measures:
- As explain in Ponce de León and Iñesta (2007), a ten-fold cross validation scheme is proposed for evaluation. Average accuracy and standard deviation are proposed measures. For multiple results comparison, an ANOVA test with Bonferroni correction is suggested.
- In the paper above, the authors provide extensive results about classification done by extracting fixed length segments from melody tracks by means of a sliding window procedure. An exploration of varying both the length of the window and the overlap between consecutive segments was performed, suggesting values for the window length above 30 bars for obtaining good classification results.
- Other publications where this corpus has been used are Pérez-Sancho et al. (2006), Ponce de León et al. (2006) and Moreno-Seco et al. (2006)
Copyright Remarks
For research purpouses only. Not for commercial use.
Citation
A Pattern Recognition Approach for Music Style Identification Using Shallow Statistical Descriptors. Ponce de León P. J. and Iñesta J. M., IEEE Transactions on Systems Man and Cybernetics C, 248-257, 37, 2007
External Links
http://grfia.dlsi.ua.es/cm/projects/prosemus/databases/jvc1+2.tgz
