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A novel audio fingerprinting method robust to time scale modification and pitch shifting

Authors:

Bilei Zhu,

Wei Li,

Zhurong Wang, and

Xiangyang XueAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

Pages 987 - 990

https://doi.org/10.1145/1873951.1874130

Published: 25 October 2010 Publication History

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Abstract

A novel audio fingerprinting method that is highly robust to Time Scale Modification (TSM) and pitch shifting is proposed. Instead of simply employing spectral or tempo-related features, our system is based on computer-vision techniques. We transform each 1-D audio signal into a 2-D image and treat TSM and pitch shifting of the audio signal as stretch and translation of the corresponding image. Robust local descriptors are extracted from the image and matched against those of the reference audio signals. Experimental results show that our system is highly robust to various audio distortions, including the challenging TSM and pitch shifting.

References

[1]

S. Baluja and M. Covell. Waveprint: efficient wavelet-based audio fingerprinting. Pattern Recognition, 41(11):3467--3480, 2008.

Digital Library

Google Scholar

[2]

R. Bardeli and F. Kurth. Robust identification of time-scaled audio. In AES 25th International Conference on Metadata for Audio, 2004.

Google Scholar

[3]

P. Cano, E. Batlle, T. Kalker, and J. Haitsma. A review of audio fingerprinting. The Journal of VLSI Signal Processing, 41(3):271--284, 2005.

Digital Library

Google Scholar

[4]

J. Haitsma and T. Kalker. A highly robust audio fingerprinting system. In International Symposium on Music Information Retrieval, pages 107--115, 2002.

Google Scholar

[5]

Y. Ke, D. Hoiem, and R. Sukthankar. Computer vision for music identification. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 597--604, 2005.

Digital Library

Google Scholar

[6]

F. Kurth, T. Gehrmann, and M. Mueller. The cyclic beat spectrum: tempo related audio features for time-scale invariant audio identification. In International Symposium on Music Information Retrieval, pages 35--40, 2006.

Google Scholar

[7]

D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004.

Digital Library

Google Scholar

[8]

K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1615--1630, 2005.

Digital Library

Google Scholar

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He HChen JChen HZeng BHuang YZhaopeng YChen X(2024)Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature PerceptionApplied Sciences10.3390/app1407311614:7(3116)Online publication date: 8-Apr-2024
https://doi.org/10.3390/app14073116
Kampelopoulos DMoysis LKarasavvidis KBoursianis AGoudos SNikolaidis S(2024)Audio Fingerprinting Method for Byzantine Hymn Recognition2024 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET60398.2024.10497021(1-4)Online publication date: 28-Mar-2024
https://doi.org/10.1109/PACET60398.2024.10497021
Liu WYang XLi ZQian FEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)SkipStreaming: Pinpointing User-Perceived Redundancy in Correlated Web Video Streaming through the Lens of ScenesProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611845(3944-3953)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611845
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Index Terms

A novel audio fingerprinting method robust to time scale modification and pitch shifting
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

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Information & Contributors

Information

Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

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He HChen JChen HZeng BHuang YZhaopeng YChen X(2024)Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature PerceptionApplied Sciences10.3390/app1407311614:7(3116)Online publication date: 8-Apr-2024
https://doi.org/10.3390/app14073116
Kampelopoulos DMoysis LKarasavvidis KBoursianis AGoudos SNikolaidis S(2024)Audio Fingerprinting Method for Byzantine Hymn Recognition2024 Panhellenic Conference on Electronics & Telecommunications (PACET)10.1109/PACET60398.2024.10497021(1-4)Online publication date: 28-Mar-2024
https://doi.org/10.1109/PACET60398.2024.10497021
Liu WYang XLi ZQian FEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)SkipStreaming: Pinpointing User-Perceived Redundancy in Correlated Web Video Streaming through the Lens of ScenesProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611845(3944-3953)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611845
Jiang YWu ZWang JXue XChang S(2018)Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.267056040:2(352-364)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1109/TPAMI.2017.2670560
Derbasov MGai VShagalova P(2018)A Hierarchical Method of Forming Fingerprints of a Sound Signal2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)10.1109/FarEastCon.2018.8602783(1-4)Online publication date: Oct-2018
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Bressan FSix JLeman MPage K(2017)Applications of duplicate detectionProceedings of the 4th International Workshop on Digital Libraries for Musicology10.1145/3144749.3144759(45-48)Online publication date: 28-Oct-2017
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Six JBressan FLeman M(2017)Applications of Duplicate Detection in Music Archives: From Metadata Comparison to Storage OptimisationDigital Libraries and Multimedia Archives10.1007/978-3-319-73165-0_10(101-113)Online publication date: 21-Dec-2017
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Ouali CDumouchel PGupta V(2016)A spectrogram-based audio fingerprinting system for content-based copy detectionMultimedia Tools and Applications10.1007/s11042-015-3081-875:15(9145-9165)Online publication date: 1-Aug-2016
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