Machine Learning for Monitoring Vocal Health and Performance of Professional Singers

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 often prove traumatic and debilitating. Yet the use of data gathering and analysis among professional singers remains rare. The work reported in this study provides a novel basis for a method via which singers, and others who use their voice professionally, can make informed investigations into the potential causes of those fluctuations, and facilitate preventative medical intervention where appropriate.