Music transcription with ISA and HMM

Emmanuel Vincent (1) , Xavier Rodet (1)

1 IRCAM - Institut de Recherche et Coordination Acoustique/Musique

We propose a new generative model for polyphonic music based on nonlinear Independent Subspace Analysis (ISA) and factorial Hidden Markov Models (HMM). ISA represents chord spectra as sums of note power spectra and note spectra as sums of instrument-dependent log-power spectra. HMM models note duration. Instrument-dependent parameters are learnt on solo excerpts and used to transcribe musical recordings as collections of notes with time-varying power and other descriptive parameters such as vibrato. We prove the relevance of our modeling assumptions by comparing them with true data distributions and by giving satisfying transcriptions of two duo recordings.

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

Emmanuel Vincent, Xavier Rodet. Music transcription with ISA and HMM. 5th Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA), Sep 2004, Granada, Spain. pp.1197--1204. ⟨inria-00544697⟩