Clustering of Musical Sounds using Polyspectral Distance Measures.

Shlomo Dubnov; Naftali Tishby

Clustering of Musical Sounds using Polyspectral Distance Measures.

Shlomo Dubnov, Naftali Tishby

Research output: Contribution to journal › Conference article › peer-review

3 Scopus citations

Abstract

This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clustering results are presented.

Original language	English
Pages (from-to)	460-466
Number of pages	7
Journal	International Computer Music Conference, ICMC Proceedings
State	Published - 1 Jan 1995
Externally published	Yes
Event	21st International Computer Music Conference, ICMC 1995 - Banff, Canada Duration: 3 Sep 1995 → 7 Sep 1995

ASJC Scopus subject areas

Music
Computer Science Applications
Media Technology

Cite this

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title = "Clustering of Musical Sounds using Polyspectral Distance Measures.",

abstract = "This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clustering results are presented.",

author = "Shlomo Dubnov and Naftali Tishby",

note = "Publisher Copyright: {\textcopyright} 1995 International Computer Music Conference, ICMC Proceedings. All rights reserved.; 21st International Computer Music Conference, ICMC 1995 ; Conference date: 03-09-1995 Through 07-09-1995",

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AU - Dubnov, Shlomo

AU - Tishby, Naftali

PY - 1995/1/1

Y1 - 1995/1/1

N2 - This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clustering results are presented.

AB - This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clustering results are presented.

UR - http://www.scopus.com/inward/record.url?scp=85159707473&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85159707473

SN - 2223-3881

SP - 460

EP - 466

JO - International Computer Music Conference, ICMC Proceedings

JF - International Computer Music Conference, ICMC Proceedings

T2 - 21st International Computer Music Conference, ICMC 1995

Y2 - 3 September 1995 through 7 September 1995

ER -

Clustering of Musical Sounds using Polyspectral Distance Measures.

Abstract

ASJC Scopus subject areas

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