Toward 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 iltes for each drum which greatly improve the separation accuracy when cymbals with long decay times are present.

Audio Engineering Society Conference: 45th International Conference: Applications of Time-Frequency Processing in Audio