Abstract
The term ‘ballroom dancing’ refers to a social and competitive type of partnered dance. Competitive ballroom dancing consists of 10 different types of dances performed to specific styles of music unique to each type of dance. There are few algorithms attempting to differentiate between pieces of music and classify them into the categories, making it hard for beginners to identify which dance corresponds to a certain piece of music they may be listening to. In our research, we attempted to use an end-to-end machine learning approach to easily and accurately classify music into the 10 different types of dance. We experimented with four types of machine learning models and received the highest accuracy of 83% using a Deep Neural Network with three hidden layers. With this algorithm, we can facilitate the learning experience of beginner ballroom dancers by aiding them to distinguish between different types of ballroom dancing music.
The two authors contributed equally to this work
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Voss, N., Nguyen, P. (2021). End-to-End Classification of Ballroom Dancing Music Using Machine Learning. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_7
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