Genre classification for million song dataset using confidence-based classifiers combination

Yajie Hu; Mitsunori Ogihara

doi:10.1145/2348283.2348480

Genre classification for million song dataset using confidence-based classifiers combination

Yajie Hu, Mitsunori Ogihara

Computer Science

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

5 Scopus citations

Abstract

We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.

Original language	English (US)
Title of host publication	SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages	1083-1084
Number of pages	2
DOIs	https://doi.org/10.1145/2348283.2348480
State	Published - Sep 28 2012
Event	35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States Duration: Aug 12 2012 → Aug 16 2012

Publication series

Name	SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other	35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
Country	United States
City	Portland, OR
Period	8/12/12 → 8/16/12

Keywords

classifier combination
song genre classification

ASJC Scopus subject areas

Information Systems

Access to Document

10.1145/2348283.2348480

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Hu, Y., & Ogihara, M. (2012). Genre classification for million song dataset using confidence-based classifiers combination. In SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1083-1084). (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/2348283.2348480

Genre classification for million song dataset using confidence-based classifiers combination. / Hu, Yajie; Ogihara, Mitsunori.

SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012. p. 1083-1084 (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval).

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

Hu, Y & Ogihara, M 2012, Genre classification for million song dataset using confidence-based classifiers combination. in SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1083-1084, 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, Portland, OR, United States, 8/12/12. https://doi.org/10.1145/2348283.2348480

Hu Y, Ogihara M. Genre classification for million song dataset using confidence-based classifiers combination. In SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012. p. 1083-1084. (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/2348283.2348480

Hu, Yajie ; Ogihara, Mitsunori. / Genre classification for million song dataset using confidence-based classifiers combination. SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012. pp. 1083-1084 (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval).

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