Quantum Machine Learning for Audio Classification with Applications to Healthcare

Michael Esposito, Glen Uehara, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented.

Original languageEnglish (US)
Title of host publication13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665463904
DOIs
StatePublished - 2022
Event13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 - Corfu, Greece
Duration: Jul 18 2022Jul 20 2022

Publication series

Name13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

Conference

Conference13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Country/TerritoryGreece
CityCorfu
Period7/18/227/20/22

Keywords

  • COVID-19
  • cough audio
  • quantum computing
  • quantum machine learning
  • quantum noise
  • spectral features

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software
  • Information Systems and Management
  • Control and Optimization
  • Communication

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