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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)
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)
This article explains evaluation methods for real-time audio to score alignment, or score following, that allow for the quantitative assessment of the robustness and precise-ness of an algorithm. The published ground truth data base and the evaluation framework, including file formats for the score and the reference alignments, are presented. The work,(More)
A system for musical accompaniment is presented in which a computer-driven orchestra follows and learns from a soloist in a concerto-like setting. The system is decomposed into three modules: the first computes a real-time score match using a hidden Markov model; the second generates the output audio by phase-vocoding a preexisting audio recording; 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 graphi-cal model containing both discrete variables, corresponding to score position, as well as a continuous latent tempo process. We use a simple data model based only on the pitch content of the audio(More)
A technique for harmonic analysis is presented that partitions a piece of music into contiguous regions and labels each with the key, mode, and functional chord, e.g. tonic, dominant, etc. The analysis is performed with a hidden Markov model and, as such, is automatically trainable from generic MIDI files and capable of finding the globally optimal harmonic(More)