Posteriograms Postprocessing for Multi-Pitch Estimation

Antentyk, Yurii

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dc.contributor.author	Antentyk, Yurii
dc.date.accessioned	2024-02-15T09:53:31Z
dc.date.available	2024-02-15T09:53:31Z
dc.date.issued	2020
dc.identifier.citation	Antentyk, Yurii. Posteriograms Postprocessing for Multi-Pitch Estimation / Antentyk, Yurii; Supervisor: Oles Dobosevych; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2020. – 30 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4523
dc.language.iso	en	uk
dc.title	Posteriograms Postprocessing for Multi-Pitch Estimation	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Music Transcription is a task of converting a musical recording into sheet music for further reproduction. The problem is still unsolved and requires a high level of expertise. Most of the works split the task into several subproblems. First of them is called frame-level transcription, which predicts the set of fundamental frequencies in the original recording for every frame. This subproblem is the main focus of this work. The solution to frame-level transcription is called a piano-roll representation - a bi- nary matrix which represents whether the given note has been played in the frame or not. However, most of the approaches do not produce a piano-roll representation in an end-to-end fashion. They rather output a posteriogram - real matrix with the same dimensions, which represents the level of uncertainty of whether note has been played during the frame. Ycart and Benetos, 2018 shows that Long Short Term mem- ory network can be trained to post-process the posteriograms and improve the piano-roll representation instead of simply cropping the posteriogram at some value. In this work, we train more robust LSTM network and experiment with different types of posteriograms.	uk