Publication Type

Conference Proceeding Article

Publication Date

8-2012

Abstract

Continuing advances in data storage and communication technologies have led to an explosive growth in digital music collections. To cope with their increasing scale, we need effective Music Information Retrieval (MIR) capabilities like tagging, concept search and clustering. Integral to MIR is a framework for modelling music documents and generating discriminative signatures for them. In this paper, we introduce a multimodal, layered learning framework called DMCM. Distinguished from the existing approaches that encode music as an ensemble of order-less feature vectors, our framework extracts from each music document a variety of acoustic features, and translates them into low-level encodings over the temporal dimension. From them, DMCM elucidates the concept dynamics in the music document, representing them with a novel music signature scheme called Stochastic Music Concept Histogram (SMCH) that captures the probability distribution over all the concepts. Experiment results with two large music collections confirm the advantages of the proposed framework over existing methods on various MIR tasks.

Keywords

Music Information Retrieval, Similarity Measure, Music Concepts

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

SIGIR '12: Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval: August 12-16, 2012: Portland, Oregon, USA

First Page

455

Last Page

464

ISBN

9781450314725

Identifier

10.1145/2348283.2348346

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/10.1145/2348283.2348346

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