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A Discriminative and Compact Audio Representation for Event Detection

Authors:

Michael W. Mahoney,

Gerald FriedlandAuthors Info & Claims

MM '16: Proceedings of the 24th ACM international conference on Multimedia

Pages 57 - 61

https://doi.org/10.1145/2964284.2970377

Published: 01 October 2016 Publication History

Abstract

This paper presents a novel two-phase method for audio representation: Discriminative and Compact Audio Representation (DCAR). In the first phase, each audio track is modeled using a Gaussian mixture model (GMM) that includes several components to capture the variability within that track. The second phase takes into account both global structure and local structure. In this phase, the components are rendered more discriminative and compact by formulating an optimization problem on Grassmannian manifolds, which we found represents the structure of audio effectively. Experimental results on the YLI-MED dataset show that the proposed DCAR representation consistently outperforms state-of-the-art audio representations: i-vector, mv-vector, and GMM.

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Cited By

de Oliveira AVentura TGanchev TSilva LMarques MSchuchmann K(2020)Speeding up training of automated bird recognizers by data reduction of audio featuresPeerJ10.7717/peerj.84078(e8407)Online publication date: 27-Jan-2020
https://doi.org/10.7717/peerj.8407
Li PXu X(2020)Recurrent Compressed Convolutional Networks for Short Video Event DetectionIEEE Access10.1109/ACCESS.2020.30039398(114162-114171)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003939
Jing LLiu BChoi JJanin ABernd JMahoney MFriedland G(2017)DCAR: A Discriminative and Compact Audio Representation for Audio ProcessingIEEE Transactions on Multimedia10.1109/TMM.2017.270393919:12(2637-2650)Online publication date: Dec-2017
https://doi.org/10.1109/TMM.2017.2703939
Show More Cited By

Index Terms

A Discriminative and Compact Audio Representation for Event Detection
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval

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cover image ACM Conferences

MM '16: Proceedings of the 24th ACM international conference on Multimedia

October 2016

1542 pages

ISBN:9781450336031

DOI:10.1145/2964284

General Chairs:
Alan Hanjalic
Delft University of Technology
,
Cees Snoek
Qualcomm Research Netherlands / University of Amsterdam
,
Marcel Worring
University of Amsterdam
,
Moderator:
Dick Bulterman
CWI / VU University Amsterdam
,
Program Chairs:
Benoit Huet
EURECOM
,
Aisling Kelliher
Virginia Tech
,
Yiannis Kompatsiaris
CERTH-ITI
,
Jin Li
Microsoft

Copyright © 2016 ACM.

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Association for Computing Machinery

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Published: 01 October 2016

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NSFC
PCSIRT
LDRD

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MM '16

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SIGMM

MM '16: ACM Multimedia Conference

October 15 - 19, 2016

Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;

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

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MM '24

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The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

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Citations

Cited By

de Oliveira AVentura TGanchev TSilva LMarques MSchuchmann K(2020)Speeding up training of automated bird recognizers by data reduction of audio featuresPeerJ10.7717/peerj.84078(e8407)Online publication date: 27-Jan-2020
https://doi.org/10.7717/peerj.8407
Li PXu X(2020)Recurrent Compressed Convolutional Networks for Short Video Event DetectionIEEE Access10.1109/ACCESS.2020.30039398(114162-114171)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003939
Jing LLiu BChoi JJanin ABernd JMahoney MFriedland G(2017)DCAR: A Discriminative and Compact Audio Representation for Audio ProcessingIEEE Transactions on Multimedia10.1109/TMM.2017.270393919:12(2637-2650)Online publication date: Dec-2017
https://doi.org/10.1109/TMM.2017.2703939
Hussein NGavves ESmeulders A(2017)Unified Embedding and Metric Learning for Zero-Exemplar Event Detection2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.225(2087-2096)Online publication date: Jul-2017
https://doi.org/10.1109/CVPR.2017.225

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