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Acoustic event diarization in TV/movie audios using deep embedding and integer linear programming

  • Yanxiong Li
  • Yuhan Zhang
  • Xianku Li
  • Mingle Liu
  • Wucheng Wang
  • Jichen YangEmail author
Article
  • 13 Downloads

Abstract

In this study, we propose a method for acoustic event diarization based on a feature of deep embedding and a clustering algorithm of integer linear programming. The deep embedding learned by deep auto-encoder network is used to represent the properties of different classes of acoustic events, and then the integer linear programming is adopted for merging audio segments belonging to the same class of acoustic events. Four kinds of TV/movie audios (21.5 h in total) are used as experimental data, including Sport, Situation comedy, Award ceremony, and Action movie. We compare the deep embedding with state-of-the-art features. Further, the clustering algorithm of integer linear programming is compared with other clustering algorithms adopted in previous works. Finally, the proposed method is compared to both supervised and unsupervised methods on four kinds of TV/movie audios. The results show that the proposed method is superior to other unsupervised methods based on agglomerative information bottleneck, Bayesian information criterion and spectral clustering, and is little inferior to the supervised method based on deep neural network in terms of acoustic event error.

Keywords

Deep embedding integer linear programming acoustic event detection audio content analysis 

Notes

Acknowledgements

The work was supported by the national natural science foundation of China (61771200, 6191101285, and 6191101306), the project of international science and technology cooperation of Guangdong province (2019A050509001), the open project program of the national laboratory of pattern recognition (NLPR) (201800004), the fundamental research funds for the central universities, South China University of Technology (Research on key techniques for analyzing complex audio scene contents, 2019), and the project of science and technology of Guangzhou (201704040062).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina

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