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Automatic recognition of vocal reactions in music listening using smart earbuds: poster abstract

Published:16 November 2020Publication History

ABSTRACT

We propose an in-ear sensing method that automatically detects vocal reactions that people often exhibit when listening to music. We observe what kind of vocal reactions are often brought during music listening and investigate the challenges of applying an existing representative acoustic classification model to vocal reaction recognition. We present our vocal reaction recognition method and the preliminary evaluation to assess its performance.

References

  1. [n.d.]. Music Listening 2019. https://www.ifpi.org/wp-content/uploads/2020/07/Music-Listening-2019-1.pdf. Accessed: September 30, 2020.Google ScholarGoogle Scholar
  2. [n.d.]. YAMNet. https://github.com/tensorflow/models/tree/master/research/audioset/yamnet. Accessed: September 23, 2020.Google ScholarGoogle Scholar
  3. Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).Google ScholarGoogle Scholar

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  1. Automatic recognition of vocal reactions in music listening using smart earbuds: poster abstract

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    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

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      Overall Acceptance Rate174of867submissions,20%
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