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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.
Particle Filtering for PLCA model with Application to Music Transcription
Automatic Music Transcription (AMT) consists in automatically estimating the notes in an audio recording, through three attributes: onset time, duration and pitch.
Deep scattering transform applied to note onset detection and instrument recognition
Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval.
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