International Journal of Music Science, Technology and Art essential requirement for anyone who needs to keep up to date with new developments of music concepts throughout the world.


journal banner

IJMSTA - Vol. 1 - Issue 1 - January 2019
ISSN 2612-2146
Pages: 8-14

A Greedy Approach to Music Voice Separation Using Hidden Markov Models

Authors: Edson E. da Silveira, Shigeki Sagayama
Categories: Journal

Abstract - This research, in the area of music information retrieval, describes a probabilistic method of separating notes into different voices for music pieces encoded as MIDI files. The proposed solution represents the music as a sequence of chords that are analyzed individually in a linear fashion. The problem of voice assignment is modelled as an HMM problem at each chord. Given a list of all previously given assignments, the model suggests the most likely voice assignments for the current chord. The model was tested on a dataset of 111 J. S. Bach pieces with F-measure scores for J. S. Bach's 15 Inventions (99.17) and J. S. Bach's The Well-Tempered Clavier 48 Preludes (95.28) and 48 Fugues (96.97) being reported. The results show improvement over currently available methods on the same dataset.

Keywords: Dynamic Programming, HMM, Voice Separation, Music Information Retrieval

Download paper

About this paper

Cite this paper as:
Edson E. da Silveira, Shigeki Sagayama (2019). A Greedy Approach to Music Voice Separation Using Hidden Markov Models. IJMSTA. 2019 Jan 7; 1 (1): 8-14.