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Sequential Modeling for Polyps Identification from the Vocal Data

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

Abstract

Given the revival of neural networks and its recent impact in other disciplines and record-breaking performances in a variety of applications, in this paper, we employed a deep sequential model for polyps detection from the vocal data. Previous research of acoustic signal recognition (ASR) has focused on hand-crafted machine learning fashion, such as Mel-frequency cepstral coefficients with hidden Markov model and Gaussian mixture model. The deep model demonstrates its flexibility and potential to outperform the traditional methods, and we expand its scope on medical symptom identification. The mapping between the raw vocal signal and the symptom recognition is established, and we show that we can achieve a good recognition accuracy, which may appear to clinical diagnosis in the near future.

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Notes

  1. 1.

    We follow the implementation from https://github.com/jameslyons/python_speech_features.

  2. 2.

    The corresponding code will be uploaded to Github for open source purpose.

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Acknowledgments

This work was supported in part by NSFC under Grant 61771342, 61731006, 61711530132, and Tianjin Higher Education Creative Team Funds Program.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, 416 Yates St., Arlington, TX, 76010, USA

    Fangqi Zhu & Qilian Liang

  2. Department of Otolaryngology Head and Neck Surgery, Peking University First Hospital, Beijing, 100034, China

    Zhen Zhong

Authors
  1. Fangqi Zhu
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  2. Qilian Liang
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  3. Zhen Zhong
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Corresponding author

Correspondence to Zhen Zhong .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Zhu, F., Liang, Q., Zhong, Z. (2020). Sequential Modeling for Polyps Identification from the Vocal Data. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_113

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_113

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

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