Unsupervised learning approach for identifying sub-genres in music scores

1. Unsupervised learning approach for identifying sub-genres in music scores Girija Shingte and Mathieu d'Aquin (@mdaquin) NUI Galway, Data Science Institute (@DSIatNUIG) Insight SFI Research Centre for Data Analytics

2. Photo by Caitlin Bussey - flickr

4. How to represent the tunes in those collections so they become comparable?

5. Symbolic music representation in MIDI Event based-representation: note-on <note: 64, velocity: 100, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 66, velocity: 80, time: 1> note-off <note: 66, velocity: 0, time: 480> note-on <note: 63, velocity: 100, time: 1> note-off <note: 63, velocity: 0, time: 240> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 66, velocity: 80, time: 1> note-off <note: 66, velocity: 0, time: 480> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240>

6. Symbolic music representation in MIDI Event based-representation: note-on <note: 64, velocity: 100, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 66, velocity: 80, time: 1> note-off <note: 66, velocity: 0, time: 480> note-on <note: 63, velocity: 100, time: 1> note-off <note: 63, velocity: 0, time: 240> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240> note-on <note: 66, velocity: 80, time: 1> note-off <note: 66, velocity: 0, time: 480> note-on <note: 64, velocity: 80, time: 1> note-off <note: 64, velocity: 0, time: 240> Can be simpliﬁed as a sequence of notes, each having to main components: Pitch: represented by the number of the note Time: represented by the number of ticks since last event.

9. Representing pitch features Basic approach: As a vector of note numbers [64,66,63,64,64,66,64] But, the same tune transposed would be completely different... Vector of differences with the first note: [0,+2,-1,0,0,+2,0] Vector of differences with the average note: [-0.43,+1.57,-1.43,-0.43,-0.43,+1.57,-0.43] Vector of differences with first note: [0,+2,-3,+1,0,+2,-2]

10. Representing time features Basic approach: Vector of durations [240,480,240,240,240,480,240] But depends on tick per beats. Vector of durations in beats [0.5,1,0.5,0.5,0.5,1,0.5] But depends on specific timing of the tune. Binary vector of note occurrence, dividing beats in 24 possible locations (here reduced to 4): [1,0,1,0,0,0,1,0,1,0,1,0,1,0,0,0,1,0] Also, beat extracted from wav files and other “spectral” features.

11. Average similarities with jigs from TheSession.org

12. Average similarities with jigs from TheSession.org

13. Clustering based on features (K-Means) Feature representation Silhouette coefficient Pitch values -0.02 Difference with first pitch -0.02 Difference with average pitch 0.03 Difference with previous note’s pitch -0.02 Note duration -0.07 Binary representation of note beats 0.2 Beats from wav files 0.159 Applied to a sample of 400 tunes from the TheSession.org with k=4

14. Pre-weighted combined features for clustering Weight pitch (avg) Weight timing 0/1 Weight beats wav Silhouette The Session Silhouette ITMA 0.33 0.33 0.34 0.31 0.19 0.2 0.2 0.6 0.22 0.03 0.45 0.45 0.1 0.17 0.25 0.55 0.25 0.2 0.31 0.19 0.1 0.1 0.8 0.33 0.21 Applied to a sample of 32 common tunes from the TheSession.org and ITMA with k=4

15. Applications Musing information retrieval Currently being extended to search across collections and to enable customising the similarity function

16. Applications Studying genres

17. Applications Studying tunes and their connections

18. Conclusions Music tunes are indeed complex objects which analysis through machine learning and data mining approaches require careful considerations. They encapsulate rich knowledge about composition, styles and culture, which we hope can be extracted. Further work need on automatically extracting comparable components of tunes, rather than treating tunes as atomic artefacts.

19. Unsupervised learning approach for identifying sub-genres in music scores Girija Shingte and Mathieu d'Aquin (@mdaquin) NUI Galway, Data Science Institute (@DSIatNUIG) Insight SFI Research Centre for Data Analytics

Unsupervised learning approach for identifying sub-genres in music scores

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Unsupervised learning approach for identifying sub-genres in music scores