Monophonic constrained non-negative sparse coding using instrument models for audio separation and transcription of monophonic source-based polyphonic mixtures

Abstract

In this paper we propose a monophonic constrained signal decomposition model applied to polyphonic signals composed of several monophonic sources from different musical instruments. The harmonic constraint is particularly effective for tonal instruments because each note is associated with a unique basis. The monophonic constraint is implemented by enforcing single-non-zero gains per instrument in the factorization process. The proposed method uses previously trained instrument models with a supervised procedure. Both constraints (harmonic and monophonic) are implemented in a deterministic manner. The proposed method has been tested for two audio signal applications, Sound Source Separation and Automatic Music Transcription. Comparison with other state-of-the-art methods using a dataset of polyphonic mixtures composed of monophonic sources has produced competitive and promising results.