@inproceedings{a66e6435875e48cbbd315d8ae3380f68,
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 ",
publisher = "Springer-Verlag",
pages = "508--517",
booktitle = "The 9th International Symposium on Computer Music Modeling and Retrieval (CMMR) Music and Emotions",
}