Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Rhythm Quantization for Transcription
Computer Music Journal
A lightweight multi-agent musical beat tracking system
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
A Review of Automatic Rhythm Description Systems
Computer Music Journal
Drum loops retrieval from spoken queries
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Tonal Harmony Analysis: A Supervised Sequential Learning Approach
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Signal-to-score music transcription using graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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A method is presented for the rhythmic parsing problem: Given a sequence of observed musical note onset times, we simultaneously estimate the corresponding notated rhythm and tempo process. A graphical model is developed that represents the evolution of tempo and rhythm and relates these hidden quantities to an observable performance. The rhythm variables are discrete and the tempo and observation variables are continuous. We show how to compute the globally most likely configuration of the tempo and rhythm variables given an observation of note onset times. Experiments are presented on both MIDI data and a data set derived from an audio signal. A generalization to computing MAP estimates for arbitrary conditional Gaussian distributions is outlined.