Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid graphical model for rhythmic parsing
Artificial Intelligence
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
Signal-to-score music transcription using graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Demonstration of music plus one: a real-time system for automatic orchestral accompaniment
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A conditional random field viewpoint of symbolic audio-to-score matching
Proceedings of the international conference on Multimedia
Real-time audio-to-score alignment using particle filter for coplayer music robots
EURASIP Journal on Advances in Signal Processing - Special issue on musical applications of real-time signal processing
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We present a new method for establishing an alignment between a polyphonic musical score and a corresponding sampled audio performance. The method uses a graphical model containing both latent discrete variables, corresponding to score position, as well as a latent continuous tempo process. We use a simple data model based only on the pitch content of the audio signal. The data interpretation is defined to be the most likely configuration of the hidden variables, given the data, and we develop computational methodology to identify or approximate this configuration using a variant of dynamic programming involving parametrically represented continuous variables. Experiments are presented on a 55-minute hand-marked orchestral test set.