Real-time beat-synchronous audio effects
NIME '07 Proceedings of the 7th international conference on New interfaces for musical expression
A leader-follower turn-taking model incorporating beat detection in musical human-robot interaction
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Creating an autonomous dancing robot
Proceedings of the 2009 International Conference on Hybrid Information Technology
Audio signal representations for indexing in the transform domain
IEEE Transactions on Audio, Speech, and Language Processing
Music tempo estimation with k-NN regression
IEEE Transactions on Audio, Speech, and Language Processing
Proceedings of the 2011 Workshop on Open Source and Design of Communication
A tempo-sensitive music search engine with multimodal inputs
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Comparing onset detection methods based on spectral features
Proceedings of the Workshop on Open Source and Design of Communication
Effects of robotic companionship on music enjoyment and agent perception
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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We present a simple and efficient method for beat tracking of musical audio. With the aim of replicating the human ability of tapping in time to music, we formulate our approach using a two state model. The first state performs tempo induction and tracks tempo changes, while the second maintains contextual continuity within a single tempo hypothesis. Beat times are recovered by passing the output of an onset detection function through adaptively weighted comb filterbank matrices to separately identify the beat period and alignment. We evaluate our beat tracker both in terms of the accuracy of estimated beat locations and computational complexity. In a direct comparison with existing algorithms, we demonstrate equivalent performance at significantly reduced computational cost