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• W2296659092 abstract "This paper proposes a Bayesian model for automatic music transcription. Automatic music transcription involves several subproblems that are interdependent of each other: multiple fundamental frequency estimation, onset detection, and rhythm/tempo recognition. In general, simultaneous estimation is preferable when several estimation problems have chicken-and-egg relationships. This paper proposes modeling the generative process of an entire music spectrogram by combining the sub-process by which a musically natural tempo curve is generated, the sub-process by which a set of note onset positions is generated based on a 2-dimensional tree structure representation of music, and the sub-process by which a music spectrogram is generated according to the tempo curve and the note onset positions. Most conventional approaches to music transcription perform note extraction prior to structure analysis, but accurate note extraction has been a difficult task. By contrast, thanks to the combined generative model, the present method performs note extraction and structure estimation simultaneously and thus the optimal solution is obtained within a unified framework. We show some of the transcription results obtained with the present method." @default.
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• W2296659092 date "2012-01-01" @default.
• W2296659092 modified "2023-10-04" @default.
• W2296659092 title "Context-free 2D tree structure model of musical notes for Bayesian modeling of polyphonic spectrograms" @default.
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