AI-POWERED BEAT DETECTION AND ITS EDUCATIONAL USES - Gudadhe, Manish; Ahluwalia, Gunveen; Kumar, Prince; Kataria, Ansh; Doni A , Ronald; Kumari, Hanna (ShodhKosh: Journal of Visual and Performing Arts, 2025)
AI powered beat recognition is a huge step forward in audio signal processing since it brings together the power of machine learning and the intricate rhythms of music. Earlier methods for the search of beats were based on energy analysis of the signal and frequency decomposition, which were many times limited by the variation of the pace, the type and the quality of the recordings. Nowadays, deep learning has led to the ability of computer systems to learn rhythmic patterns from large datasets. This allows beats to be found in a much greater variety of musical styles more accurately and adaptively. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) work with the data particularly well because they extract out the hierarchical time patterns as well as finds relationships between events in audio data. The effects this technology will have on the way we teach are huge. AI-driven beat recognition in music classes helps to improve rhythm training by providing students with feedback in real time in order to help them improve their timing and rhythmic awareness. In addition to the normal means of teaching music, AI beat recognition enables interesting learning tools and games with rhythm, as well as virtual instruments that change according to the action of the user. These systems promote engagement in both the online and school environments through learning environments that are both flexible and personalized with feedback and apps that tie music and math to cognitive science. Case studies show that platforms with AI beats recognition make learning more fun, keeps students motivated and helps them to understand the rhythmic ideas.