Christopher Raphael

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In this paper we address an important step towards our goal of automatic musical accompaniment | the segmentation problem. Given a score to a piece of monophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions corresponding to the notes and rests in the score. Within the(More)
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(More)
A hidden Markov model approach to piano music transcription is presented. The main difficulty in applying traditional HMM techniques is the large number of chord hypotheses that must be considered. We address this problem by using a trained likelihood model to generate reasonable hypotheses for each frame and construct the search graph out of these(More)
We present a technique that, given a sequence of musical note onset times, performs simultaneous identi cation of the notated rhythm and the variable tempo associated with the times. Our formulation is probabilistic: We develop a stochastic model for the interconnected evolution of a rhythm process, a tempo process, and an observable process. This model(More)
PURPOSE Use of accelerometers to assess physical activity (PA) is widespread in public health research, but their utility is often limited by the accuracy of data-processing techniques. We hypothesized that more sophisticated approaches to data processing could distinguish between activity types based on accelerometer data, providing a more accurate picture(More)
We present a set of techniques for omnifont, unlimited-vocabulary OCR, within the context of a system based on Hidden Markov Models (HMM). First, we address the issue of how to perform OCR on omnqont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is(More)