Live Drum Separation Using Probabilistic Spectral Clustering Based on the Itakura-Saito Divergence

We present a live drum separation system for a specific target drumset to be used as a front end in a complete live drum understanding system. Our system decomposes drum note onsets onto spectral drum templates by adapting techniques from non-negative matrix factorization. Multiple templates per drum are computed using a new Gamma mixture model clustering procedure to account for the variety of sounds that can be produced by a single drum. This clustering procedure imposes an Itakura-Saito distance metric on the cluster space. In addition, we utilize “tail” templates for each drum which greatly improve the separation accuracy when cymbals with long decay times are present.

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