The novel approach of estimating activity mode, rather than activity level, may allow for more accurate field-based estimates of physical activity using accelerometer data, and this approach warrants more study in a larger and more diverse population of subjects and activities.

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 framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning of the the data model parameters, and compute the segmentation that globally minimizes the posterior expected number of segmentation errors. We also show how to produce \on-line" estimates of score position. We present examples of our experimental results and readers are encouraged to access actual sound data we have made available from these experiments.

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By relating musical sound to musical notation, these systems generate tireless, expressive musical accompaniment to follow and sometimes learn from a live human performance.

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.