Instrument identification in polyphonic music: Feature weighting to minimize influence of sound overlaps

Tetsuro Kitahara*, Masataka Goto, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. When multiple instruments simultaneously play, partials (harmonic components) of their sounds overlap and interfere, which makes the acoustic features different from those of monophonic sounds. To cope with this, we weight features based on how much they are affected by overlapping. First, we quantitatively evaluate the influence of overlapping on each feature as the ratio of the within-class variance to the between-class variance in the distribution of training data obtained from polyphonic sounds. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. In addition, we improve instrument identification using musical context. Experimental results showed that the recognition rates using both feature weighting and musical context were 84.1 % for duo, 77.6 % for trio, and 72.3 % for quartet; those without using either were 53.4, 49.6, and 46.5 % , respectively.

Original languageEnglish
Article number51979
JournalEurasip Journal on Advances in Signal Processing
Publication statusPublished - 2007
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Hardware and Architecture


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