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Investigation on the use of Hidden-Markov Models in automatic transcription of music
Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i. e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i. e. correcting note segmentation using training data.
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