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Audio similarity measure by graph modeling and matching

Published: 23 October 2006 Publication History

Abstract

This paper proposes a new approach for the similarity measure and ranking of audio clips by graph modeling and matching. Instead of using frame-based or salient-based features to measure the acoustical similarity of audio clips, segment-based similarity is proposed. The novelty of our approach lies in two aspects: segment-based representation, and the similarity measure and ranking based on four kinds of similarity factors. In segmentbased representation, segments not only capture the change property of audio clip, but also keep and present the change relation and temporal order of audio features. In the similarity measure and ranking, four kinds of similarity factors: acoustical, granularity, temporal order and interference are progressively and jointly measured by optimal matching and dynamic programming, which guarantee the comprehensive and sufficient similarity measure between two audio clips. The experimental result shows that the proposed approach is better than some existing methods in terms of retrieval and ranking capabilities.

References

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Cited By

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  • (2024)Audio Similarity Detection2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC63619.2025.10848704(1-6)Online publication date: 3-Dec-2024
  • (2009)Audio retrieval by segment-based manifold-rankingProceedings of the 2009 IEEE international conference on Multimedia and Expo10.5555/1698924.1699093(686-689)Online publication date: 28-Jun-2009
  • (2009)Learning to rank videos personally using multiple cluesProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1646396.1646446(1-8)Online publication date: 8-Jul-2009
  • Show More Cited By

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  1. Audio similarity measure by graph modeling and matching

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    cover image ACM Conferences
    MM '06: Proceedings of the 14th ACM international conference on Multimedia
    October 2006
    1072 pages
    ISBN:1595934472
    DOI:10.1145/1180639
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 23 October 2006

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    Author Tags

    1. audio retrieval
    2. audio similarity measure

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    MM06
    MM06: The 14th ACM International Conference on Multimedia 2006
    October 23 - 27, 2006
    CA, Santa Barbara, USA

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    View all
    • (2024)Audio Similarity Detection2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC63619.2025.10848704(1-6)Online publication date: 3-Dec-2024
    • (2009)Audio retrieval by segment-based manifold-rankingProceedings of the 2009 IEEE international conference on Multimedia and Expo10.5555/1698924.1699093(686-689)Online publication date: 28-Jun-2009
    • (2009)Learning to rank videos personally using multiple cluesProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1646396.1646446(1-8)Online publication date: 8-Jul-2009
    • (2009)Audio retrieval by segment-based manifold-ranking2009 IEEE International Conference on Multimedia and Expo10.1109/ICME.2009.5202589(686-689)Online publication date: Jun-2009

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