This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times (or note values) and, thus, could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.
This paper proposes a Bayesian approach for automatic music transcription of polyphonic MIDI signals based on generative modeling of onset occurrences of musical notes. Automatic music transcription involves two subproblems that are interdependent of each other: rhythm recognition and tempo estimation. When we listen to music, we are able to recognize its rhythm and tempo (or beat location) fairly easily even though there is ambiguity in determining the individual note values and tempo. This may be made possible through our empirical knowledge about rhythm patterns and tempo variations that possibly occur in music. To automate the process of recognizing the rhythm and tempo of music, we propose modeling the generative process of a MIDI signal of polyphonic music by combining the sub-process by which a musically natural tempo curve is generated and the sub-process by which a set of note onset positions is generated based on a 2-dimensional rhythm tree structure representation of music, and develop a parameter inference algorithm for the proposed model. We show some of the transcription results obtained with the present method. Abstract This article presents a probabilistic model of polyphonic music analysis. Taking a note pattern as input, the model combines three aspects of symbolic music analysis—metrical analysis, harmonic analysis, and stream segregation—into a single process, allowing it to capture the complex interactions between these structures. The model also yields an estimate of the probability of the note pattern itself; this has implications for the modelling of music transcription. I begin by describing the generative process that is assumed and the analytical process that is used to infer metrical, harmonic, and stream structures from a note pattern. I then present some tests of the model on metrical analysis and harmonic analysis, and discuss ongoing work to integrate the model into a transcription system.  We present a probabilistic generative 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. Exact 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 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. Combining a stochastic model of deviating note durations and a stochastic grammar representing possible sequences of notes, the maximum likelihood estimate of the note sequence is searched in terms of Viterbi algorithm. The same principle is successfully applied to a joint problem of bar line allocation, time measure recognition, and tempo estimation. Finally, durations of consecutive spl eta n notes are combined to form a "rhythm vector" representing tempo-free relative durations of the notes and treated in the same framework. Significant improvements compared with conventional "quantization" techniques are shown. 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. Musical time can be considered to be the product of two time scales: the discrete time intervals of a metrical structure and the continuous time scales of tempo changes and expressive timing (Clarke 1987a). In musical notation both kinds are present, although the notation of continuous time is less developed than that of metric time (often just a word like "rubato" or "accelerando" is notated in the score). In the experimental literature, different ways in which a musician can add continuous timing changes to the metrical score have been identified. There are systematic changes in certain rhythmic forms: for example, shortening triplets (Vos and Handel 1987) and timing differences occurring in voice leading with ensemble playing (Rasch 1979). Deliberate departures from metricality, such as rubato, seem to be used to emphasize musical struc- ture, as exemplified in the phrase-final lengthening principle formalized by Todd (1985). In addition to these effects, which are collectively called expressive timing, there are nonvoluntary effects, such as random timing errors caused by the limits in the accuracy of the motor system (Shaffer 1981) and errors in mental time-keeping processes (Vorberg and Hambuch 1978). These effects are generally rather small - in the order of 10-100 msec. To make sense of most musical styles, it is necessary to separate the discrete and continuous components of musical time. We will call this process of separation quantization, although the term is generally used to reflect only the extraction of a metrical score from a musical performance. In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model HMM that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper, we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs, for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons. From the Publisher: Can humans compute? This is the question to which H. Christopher Longuet-Higgins, one of the founding figures of cognitive science, has devoted his research over the past twenty years. His and his field's intellectual odyssey from the fringe to the center of the scientific world's attention is recounted with wit and grace in this wide-ranging collection of previously published and original essays. The volume begins in the late 1960s, when the author had moved from theoretical chemistry to what was then known as theoretical biology. It traces his search for new concepts with which to establish a science of the mind, and it includes Longuet-Higgins's famous comment on the 1971 Lighthill Report in which he introduced the term "cognitive science" and sketched the possible components of the field. The essays are divided into five parts. The first, Generalities, explores the basic philosophical questions at the root of the new science. The essays on Music show the importance of the musical sense as a testing ground for understanding cognitive processes in general. The author's forays into Language describe some of the major early achievements in the now very active field of computational linguistics. The studies of Vision are all directed to the problem - crucial for the development of machine-vision systems - of inferring the structure of a scene from two views. The author suggests that the chapters on Memory "be treated indulgently as the first attempt of a physical scientist to climb out of the mindless world of atoms and molecules into the real world of subjective experience." H. Christopher Longuet-Higgins is Royal Society ResearchProfessor at the University of Sussex. Mental Processes inaugurates the series Explorations in Cognitive Science, edited by Margaret Boden and co-sponsored by The MIT Press and The British Psychological Society. A Bradford Book.