A feature survey for emotion classification of western popular music

Scott Beveridge; Don Knox

A feature survey for emotion classification of western popular music

Scott Beveridge, Don Knox

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we propose a feature set for emotion classification of Western popular music. We show that by surveying a range of common feature extraction methods, a set of five features can model emotion with good accuracy. To evaluate the system we implement an independent feature evaluation paradigm aimed at testing the property of generalizability; the ability of a machine learning algorithm to maintain good performance over different data sets.

Original language	English
Title of host publication	The 9th International Symposium on Computer Music Modeling and Retrieval (CMMR) Music and Emotions
Publisher	Springer-Verlag
Pages	508-517
Number of pages	10
Publication status	Published - Jun 2012

Publication series

Name	Lecture Notes in Computer Sciences
Publisher	Springer

Keywords

music emotion classification
popular music
support vector machine

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title = "A feature survey for emotion classification of western popular music",

abstract = "In this paper we propose a feature set for emotion classification of Western popular music. We show that by surveying a range of common feature extraction methods, a set of five features can model emotion with good accuracy. To evaluate the system we implement an independent feature evaluation paradigm aimed at testing the property of generalizability; the ability of a machine learning algorithm to maintain good performance over different data sets.",

keywords = "music emotion classification, popular music, support vector machine",

author = "Scott Beveridge and Don Knox",

year = "2012",

month = jun,

language = "English",

series = "Lecture Notes in Computer Sciences ",

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AU - Knox, Don

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AB - In this paper we propose a feature set for emotion classification of Western popular music. We show that by surveying a range of common feature extraction methods, a set of five features can model emotion with good accuracy. To evaluate the system we implement an independent feature evaluation paradigm aimed at testing the property of generalizability; the ability of a machine learning algorithm to maintain good performance over different data sets.

KW - music emotion classification

KW - popular music

KW - support vector machine

M3 - Conference contribution

T3 - Lecture Notes in Computer Sciences

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EP - 517

BT - The 9th International Symposium on Computer Music Modeling and Retrieval (CMMR) Music and Emotions

PB - Springer-Verlag

ER -