Publication Type

Conference Proceeding Article

Publication Date

7-2010

Abstract

Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this paper, we introduce a novel system called MMTagger that effectively integrates both multimodal and temporal information in the representation of music signal. The carefully designed multilayer architecture of the proposed classification framework seamlessly combines Multiple Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) into a single framework. The structure preserves more discriminative information, leading to more accurate and robust tagging. Experiment results obtained with two large music collections highlight the various advantages of our multilayer framework over state of the art techniques.

Keywords

Browsing, Music information retrieval, Search, Tagging

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

SIGIR 2010: Proceedings: 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: Geneva, Switzerland, July 19-23, 2010

First Page

635

Last Page

642

ISBN

9781450301534

Identifier

10.1145/1835449.1835555

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/1835449.1835555

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