Drum transcription using partially fixed non-negative matrix factorization

In this paper, a drum transcription algorithm using partially fixed non-negative matrix factorization is presented. The proposed method allows users to identify percussive events in complex mixtures with a minimal training set. The algorithm decomposes the music signal into two parts: percussive part with pre-defined drum templates and harmonic part with undefined entries. The harmonic part is able to adapt to the music content, allowing the algorithm to work in polyphonic mixtures. Drum event times can be simply picked from the percussive activation matrix with onset detection. The system is efficient and robust even with a minimal training set. The recognition rates for the ENST dataset vary from 56.7 to 78.9% for three percussive instruments extracted from polyphonic music.