Quantum Machine Learning for Audio Classification with Applications to Healthcare
13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2120609
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. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS