Song-level multi-pitch tracking by heavily constrained clustering

Zhiyao Duan*, Jinyu Han, Bryan A Pardo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Given a set of monophonic, harmonic sound sources (e.g. human voices or wind instruments), multi-pitch estimation (MPE) is the task of determining the instantaneous pitches of each source. Multi-pitch tracking (MPT) connects the instantaneous pitch estimates provided by MPE algorithms into pitch trajectories of sources. A trajectory can be short (within a musical note), or long (an entire piece of music). While note-level MPT methods usually utilize local time-frequency proximity of pitches to connect them into a note, songlevel MPT is much more difficult and needs more information. This is because pitches evolve discontinuously from note to note, and pitch trajectories can even interweave. In this paper, we cast the song-level MPT problem as a constrained clustering problem. The constraints are time-frequency locality of pitches and the clustering objective is their timbre consistency. Due to this problem's unique properties, existing constrained clustering algorithms cannot be directly applied. We propose a new constrained clustering algorithm. Experiments show that our approach produces good results on real-world music recordings of 4 musical instruments.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages57-60
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Constrained clustering
  • Fundamental frequency
  • Multi-pitch estimation
  • Pitch tracking

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Duan, Z., Han, J., & Pardo, B. A. (2010). Song-level multi-pitch tracking by heavily constrained clustering. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 57-60). [5496224] https://doi.org/10.1109/ICASSP.2010.5496224