Transcribing Bach Chorales using non-negative matrix factorisation

This paper discusses our research on polyphonic music transcription using non-negative matrix factorization (NMF). The application of NMF in polyphonic transcription has two known limitations (i) the transcription output is a permutation of the input source signals (e.g., the following polyphonic input notes c, e, g and b may produce polyphonic output notes in the following order c, b, g and e) and (ii) the accuracy of the transcription depends on the accuracy of the factor r where r is the actual number of active pitches. This work proposes a novel approach by exploiting a tone model to tackle both the permutation of transcription output and the unknown factoring r issues. In our current implementation, the tone model is learned from the training data consisting of the pitches of the desired instrument. This approach offers an effective exploitation of the domain knowledge (i.e., tone model of each pitch). The empirical results show that the proposed tone-model initialised NMF (ICTM-NMF) could significantly improve the transcription output accuracy.