Detecting Music Genre Using Extreme Gradient Boosting

被引:13
作者
Murauer, Benjamin [1 ]
Specht, Guenther [1 ]
机构
[1] Univ Innsbruck, Innsbruck, Austria
来源
COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018) | 2018年
关键词
NEURAL-NETWORKS;
D O I
10.1145/3184558.3191822
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper summarizes our contribution to the CrowdAl music genre classification challenge "Learning to Recognise Musical Genre from Audio on the Web" as part of the WebConference 2018. We utilize different approaches from the field of music analysis to predict the music genre of given mp3 music files, including a convolutional neural network for spectrogram classification, deep neural networks and ensemble methods using various numerical audio features. Our best results were obtained by an extreme gradient boosting classifier.
引用
收藏
页码:1923 / 1927
页数:5
相关论文
共 16 条
[1]  
[Anonymous], 2016, MIREX 2016
[2]  
[Anonymous], 2016, KDD16 P 22 ACM, DOI DOI 10.1145/2939672.2939785
[3]  
Bogdanov Dmitry, 2017, P MEDIAEVAL 2017 WOR
[4]  
Chen N., 2017, P 18 ISMIR C SUZH CH, P509
[5]  
Choi Keunwoo, 2016, ABS160904243 CORR
[6]   An evaluation of Convolutional Neural Networks for music classification using spectrograms [J].
Costa, Yandre M. G. ;
Oliveira, Luiz S. ;
Silla, Carlos N., Jr. .
APPLIED SOFT COMPUTING, 2017, 52 :28-38
[7]  
Defferrard M., 2017, ISMIR, P316
[8]   Recurrent neural networks for music computation [J].
Franklin, Judy A. .
INFORMS JOURNAL ON COMPUTING, 2006, 18 (03) :321-338
[9]   Extremely randomized trees [J].
Geurts, P ;
Ernst, D ;
Wehenkel, L .
MACHINE LEARNING, 2006, 63 (01) :3-42
[10]   Deep Image Features in Music Information Retrieval [J].
Gwardys, Grzegorz ;
Grzywczak, Daniel .
INTERNATIONAL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS, 2014, 60 (04) :321-326