Determining feature relevance in subject responses to musical stimuli

Morwaread M. Farbood, Bernd Schoner

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

Abstract

This paper presents a method that determines the relevance of a set of signals (musical features) given listener judgments of music in an experimental setting. Rather than using linear correlation methods, we allow for nonlinear relationships and multi-dimensional feature vectors. We first provide a methodology based on polynomial functions and the least-mean-square error measure. We then extend the methodology to arbitrary nonlinear function approximation techniques and introduce the Kullback-Leibler Distance as an alternative relevance metric. The method is demonstrated first with simple artificial data and then applied to analyze complex experimental data collected to examine the perception of musical tension.

Original languageEnglish (US)
Title of host publicationMathematics and Computation in Music
Subtitle of host publicationSecond International Conference, MCM 2009, John Clough Memorial Conference, Proceedings
Pages115-129
Number of pages15
DOIs
StatePublished - 2009

Publication series

NameCommunications in Computer and Information Science
Volume38
ISSN (Print)1865-0929

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

Fingerprint Dive into the research topics of 'Determining feature relevance in subject responses to musical stimuli'. Together they form a unique fingerprint.

  • Cite this

    Farbood, M. M., & Schoner, B. (2009). Determining feature relevance in subject responses to musical stimuli. In Mathematics and Computation in Music: Second International Conference, MCM 2009, John Clough Memorial Conference, Proceedings (pp. 115-129). (Communications in Computer and Information Science; Vol. 38). https://doi.org/10.1007/978-3-642-02394-1_11