We present a probabilistic generarive model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Ex- act computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.

This paper describes a method for organizing onset times performed along a jam-session accompaniment into normalized (quantized) positions in a score so the performance data can be stored in a reusable form. Unlike most previous beat-tracking-related methods that focus on predicting or estimating beat positions, our method deals with the problem of eliminating the onset-time deviations under the condition that the beat positions are given. Our method solves this problem by using hidden Markov models (HMMs) that model onset-time transition and deviation. The HMM parameters are obtained by unsupervised estimation using the Baum-Welch algorithm and held-out interpolation: they can be derived from only the session recording that we wanted to quantize. Experimental results show that our model performs better than the semi-automatic quantization in commercial sequencing software. 1

This paper describes a Hidden Markov Model (HMM)-based method of automatic transcription of MIDI (Musical Instrument Digital Interface) signals of performed music. The problem is formulated as recognition of a given sequence of fluctuating note durations to find the most likely intended note sequence utilizing the modern continuous speech recognition technique.

This paper presents a rhythm recognition technique based on a probabilistic approach by utilizing generative model for timing information in expressive music performance. The problem of rhythm recognition including rhythm parsing and tempo tracking, is to retrieve information of rhythm and tempo from a sequence of observed note durations. Since performed note length deviates in real performance and decomposition of the duration into rhythm and tempo is not unique in general, this problem must be solved in a probabilistic approach. We formulate rhythm recognition as maximum a posteriori (MAP) state sequence estimation among a finite state network of Hidden Markov Models (HMMs). The structure of the proposed stochastic model is almost equivalent to a network model of HMMs used in continuous speech recognition technique. The most likely rhythm and tempo in this probabilistic model are obtained by use of an effective search algorithm, level building. Experimental evaluation using MIDI recordings of a classical music piece is also reported.