Machine Learning for Monitoring Vocal Health and Performance of Professional Singers : WestminsterResearch

Title	Machine Learning for Monitoring Vocal Health and Performance of Professional Singers
Authors	Reni, S., Jones, S. and Kale, I.
Type	Conference paper
Abstract	This paper gives an insight into interdisciplinary research examining the use of machine learning techniques to monitor the vocal health of professional singers. The work reported establishes the viability of using a dataset of audio samples of the human voice to train a convolutional neural network to assess fluctuations in vocal performance of professional singers. Variations in the ease and quality of vocal production are a common experience among those who rely on their voice for a living, and vocal health issues can be traumatic and debilitating. Yet the use of data gathering and analysis among professional singers remains rare. The work reported in this study to provides a basis for singers, and others who use their voice professionally, to make informed investigations into the potential causes of those fluctuations, and to facilitate preventative medical intervention where appropriate.
Keywords	AI
	ML
	Speech processing
	MEL spectogram
Year	2024
Conference	2024 IEEE International Symposium on Circuits and Systems
Publisher	IEEE
Accepted author manuscript	ISACA24_VocalNNPaper_RevisionFINAL.pdf License CC BY 4.0 File Access Level Open (open metadata and files)
Web address (URL)	https://2024.ieee-iscas.org/

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Machine Learning for Monitoring Vocal Health and Performance of Professional Singers

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