Harmonic and inharmonic nonnegative matrix factorization for polyphonic pitch transcription

Emmanuel Vincent (1) , Nancy Bertin (2) , Roland Badeau (2)

1 METISS - Speech and sound data modeling and processing
2 Télécom ParisTech

Polyphonic pitch transcription consists of estimating the onset time, duration and pitch of each note in a music signal. This task is difficult in general, due to the wide range of possible instruments. This issue has been studied using adaptive models such as Nonnegative Matrix Factorization (NMF), which describe the signal as a weighted sum of basis spectra. However basis spectra representing multiple pitches result in inaccurate transcription. To avoid this, we propose a family of constrained NMF models, where each basis spectrum is expressed as a weighted sum of narrowband spectra consisting of a few adjacent partials at harmonic or inharmonic frequencies. The model parameters are adapted via combined multiplicative and Newton updates. The proposed method is shown to outperform standard NMF on a database of piano excerpts.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Emmanuel Vincent, Nancy Bertin, Roland Badeau. Harmonic and inharmonic nonnegative matrix factorization for polyphonic pitch transcription. 2008 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Mar 2008, Las Vegas, United States. pp.109--112. ⟨inria-00544183⟩