Drums transcription
This work is about the automatic transcription of drum sequences. We use the audio tracks from the ENST-Drums database.
We distinguish 4 different taxonomies of instruments:
- Gillet's taxonomy: Bass drum, snare drum, hi-hat;
- Super-category: Membrane, plate;
- Basic-level: Bass drum, snare drum, tom, hi-hat, cymbal;
- Sub-category: Bass drum, snare drum, lowest tom, low tom, low mid tom, mid tom, open hi-hat, closed hi-hat, ride cymbal, crash cymbal, splash cymbal, other cymbal.
Super-category, basic-level, and sub-category were defined by Herrera et al. in (4). Gillet's taxonomy is the taxonomy used by Gillet in his thesis (1).
For most of this work, we placed ourselves in similar conditions as in Gillet's thesis.
We use the midi-one tracks of the database, without accompaniment (i.e. noise -∞ db). We focus mainly on the Gillet taxonomy, in order to be able to compare our results with his results.
Segmentation
In his thesis, Gillet does onset detection in order to detect when a stroke occurs. In our case, onset detection isn't really the point of the exercise. That is why we use the ground truth as an oracle to extract the segments corresponding to strokes.
The difficulty is when several instruments are played (nearly) simultaneously. We consider that instruments which are played within the same window of 50 ms belong to the same segment.
Features
We use the same features as in (2):
- The means of the first 13 MFCC coefficients, using an analysis window of 2048 frames, with a 50% overlap;
- 4 spectral shape parameters as defined by Gillet: spectral centroïd, width, skewness, and kurtosis. This feature is called SpectralShapeStatistics in Yaafe;
- The last feature, band-wise frequency content parameters doesn't exist in Yaafe, so we don't use it for now.
The set all features contains a more exhaustive list of features. The set selected features, contains features collected by the IRMFSP algorithm.
Classification
There are two ways of handling classification:
- One single classifier, with 2N classes (one for each combination of instruments).
- Or N binary classifiers, with N classes (one for each instrument), that decides if the instrument is played or not.
We try two different classifiers: a SVM classifier with RBF kernel as in (2), and a K-NN as in (4).
Results
Gillet, SVM (C=2, σ=1)
| Instrument | Precision | Recall | F-measure |
|---|---|---|---|
| Bass drum | 91.4% | 74.1% | 81.9% |
| Snare drum | 93.0% | 79.6% | 85.8% |
| Hi-hat | 84.7% | 93.2% | 88.8% |
| Average | 89.7% | 82.3% | 85.5% |
Basic-level, SVM (C=2, σ=1)
| Instrument | Precision | Recall | F-measure |
|---|---|---|---|
| Average | 87.7% | 65.8% | 72.7% |
Sub-category, SVM (C=2, σ=1)
| Instrument | Precision | Recall | F-measure |
|---|---|---|---|
| Average | 81.3% | 44.4% | 52.3% |
Gillet, 3 binary K-NN, all features (k=5)
| Instrument | Precision | Recall | F-measure |
|---|---|---|---|
| Bass drum | 86.2% | 87.5% | 86.8% |
| Snare drum | 89.8% | 92.7% | 91.2% |
| Hi-hat | 89.7% | 94.8% | 92.2% |
| Average | 88.5% | 91.7% | 90.1% |
For more results, visit this page.
Bibliography
- O. Gillet. Transcription des signaux percussifs. Application à l'analyse de scènes musicales audiovisuelles. PhD thesis, 2007.
- O. Gillet and G. Richard. Automatic transcription of drum loops. 2004.
- O. Gillet and G. Richard. Enst-drums: an extensive audio-visual database for drum signals processing. 2006.
- P. Herrera, A. Yeterian, R. Yeterian and F. Gouyon. Automatic Classification of Drum Sounds: A Comparison of Feature Selection and Classification Techniques. 2002.
Gillet's thesis is quite big, so here is a small index (the section that is interesting for us is the section 4).
- Taxonomy description: p.54
- Attribute extraction: p.61
- Attribute selection: p.65 and p.91
- Classification parameters: p.67 and p.93
- HMM: p.74
- Database: p.84
- Classification results: p.88
Dependencies
- Yaafe, http://yaafe.sourceforge.net/
- Scikit-learn, http://scikit-learn.org/
Python resources
- Machine learning, http://scikit-learn.org/
- Features extraction, http://yaafe.sourceforge.net/
- Wave audio files, http://docs.python.org/2/library/wave.html
- Signal processing, http://docs.scipy.org/doc/scipy/reference/signal.html
- Plot drawing, http://matplotlib.org/
- Feature selection, http://scikit-learn.org/stable/modules/feature_selection.html
- Onset detection tool, https://github.com/johnglover/modal