SCIENCE@home

Modeling concept dynamics for large scale music search

Jialie Shen,HweeHwa Pang,Meng Wang,Shuicheng Yan

Published 2012 in Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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.

PUBLICATION RECORD

  • Publication year

    2012

  • Venue

    Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

  • Publication date

    2012-08-12

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1145/2348283.2348346
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-37 of 37 references · Page 1 of 1

CITED BY

Showing 1-20 of 20 citing papers · Page 1 of 1