Towards a computational model of melody identification in polyphonic music

Authors:
Søren Tjagvad Madsen;Gerhard Widmer
Affiliations:
Austrian Research Institute for Artificial Intelligence, Vienna;Department of Computational Perception Johannes Kepler University, Linz
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

Citing 7
Cited 3

Manipulation of music for melody matching

MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Artificial Perception and Music Recognition

Artificial Perception and Music Recognition
Information Retrieval

Information Retrieval
A Musical Pattern Discovery System Founded on a Modeling of Listening Strategies

Computer Music Journal
Query by humming with the VocalSearch system

Communications of the ACM - Music information retrieval
Structural and affective aspects of music from statistical audio signal analysis: Special Topic Section on Computational Analysis of Style

Journal of the American Society for Information Science and Technology
Melodic analysis with segment classes

Machine Learning

Melodic Track Identification in MIDI Files Considering the Imbalanced Context

IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Feature extraction using pitch class profile information entropy

MCM'11 Proceedings of the Third international conference on Mathematics and computation in music
Automatic reduction of MIDI files preserving relevant musical content

AMR'08 Proceedings of the 6th international conference on Adaptive Multimedia Retrieval: identifying, Summarizing, and Recommending Image and Music

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents first steps towards a simple, robust computational model of automatic melody identification. Based on results from music psychology that indicate a relationship between melodic complexity and a listener's attention, we postulate a relationship between musical complexity and the probability of a musical line to be perceived as the melody. We introduce a simple measure of melodic complexity, present an algorithm for predicting the most likely melody note at any point in a piece, and show experimentally that this simple approach works surprisingly well in rather complex music.