Alternate Level Clustering for Drum Transcription

Mathias Rossignol (1) , Mathieu Lagrange (2) , Grégoire Lafay (2) , Emmanouil Benetos (3)

1 STMS - Sciences et Technologies de la Musique et du Son
2 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
3 QMUL - Queen Mary University of London

This paper introduces a clustering-based unsupervised approach to the problem of drum transcription. The proposed method is based on a stack of multiple clustering and seg-mentation stages that progressively build up meaningful audio events, in a bottom-up fashion. At each level, the inherent redundancy of the repeating events guides the clustering of objects into more complex structures. Comparison with state-of-the-art approaches demonstrate the potential of the proposed approach, both in terms of efficiency and of ability to generalize.

Mots clés: unsupervised learning; drum transcription; Index Terms— Audio segmentation; automatic music transcription

Domaines: Machine Learning [stat.ML]

Mathias Rossignol, Mathieu Lagrange, Grégoire Lafay, Emmanouil Benetos. Alternate Level Clustering for Drum Transcription. EUSIPCO, Sep 2015, Nice, France. ⟨hal-01122006v1⟩