Dancing Guide: Near Realtime Audio Classification of Live Dance Music on Smartphones

Affiliation: Seewald Solutions, Lärchenstraße 1, A-4616 Weißkirchen a.d. Traun, Austria

ISBN: 978-989-758-484-8

ISSN: 2184-433X

Keyword(s): Audio Classification, Live Music, Dance Music, Smartphones, Music Genre Classification, Near-real-Time.

Abstract: Between 2008 and 2014 we developed and deployed a live music classification system, Dancing Guide, to be run on Android and iPhones mobile phones in near realtime. Although internet access was needed to send feedback and classifications to the server for statistical purposes, the music classification system also worked offline without any loss in accuracy or speed. This is essential since in most discos and dancing schools, internet access is spotty at best. During the seven years of the project, the app was available both for iPhone and Android, initially in German and English, but later – thanks to volunteer translations – also in Czech, Spanish, French, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Russian and Traditional Chinese. Measured by user feedback, we achieved an accuracy of roughly 73% at a coverage of 61%. While the accuracy is comparable to initial estimates using cross-validation, the coverage is much worse. Background noise – which we did not model – or the li mited feature set may have been responsible. We retrained the system several times, however performance did not always improve, so we sometimes left the previously trained system in place. In the end, the limited feature set which was initially chosen prevented further improvement of coverage and accuracy, and we stopped further development.

Dancing Guide: Near Realtime Audio Classification of Live Dance Music on Smartphones

Topics: Ambient Intelligence; Industrial Applications of AI; Machine Learning; Vision and Perception