Drum Music Transcription Using Prior Subspace Analysis and Pattern Recognition

Andrio Spich; Massimiliano Zanoni; Augusto Sarti; Stefano Tubaro
DAFx-2010 - Graz
Polyphonic music transcription has been an active field of research for several decades, with significant progress in past few years. In the specific case of automatic drum music transcription, several approaches have been proposed, some of which based on feature analysis, source separation and template matching. In this paper we propose an approach that incorporates some simple rules of music theory with the goal of improving the performance of conventional low-level drum transcription methods. In particular, we use Prior Subspace Analysis for early drum transcription, and we statistically process its output in order to recognize drum patterns and perform error correction. Experiments on polyphonic popular recordings showed that the proposed method improved the transcription accuracy of the original transcription results from 75% to over 90%.