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Music Performance Rendering

3260 papers • 126 benchmarks • 313 datasets

Music performance rendering is the task of generating human-like performances for written musical scores.

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Most implemented papers

VirtuosoNet: A Hierarchical RNN-based System for Modeling Expressive Piano Performance

Juhan Nam, Taegyun Kwon, Kyogu Lee, Dasaem Jeong, Yoojin Kim•Mon Dec 31 2018

This model consists of recurrent neural networks with hierarchical attention and conditional variational autoencoder that takes a sequence of note-level score features extracted from MusicXML as input and predicts piano performance features of the corresponding notes.

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Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance

Juhan Nam, Taegyun Kwon, Dasaem Jeong, Yoojin Kim•Thu May 23 2019

This paper designs the model using note-level gated graph neural network and measure-level hierarchical attention network with bidirectional long shortterm memory with an iterative feedback method and applies it for rendering expressive piano performance from the music score.

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Reconstructing Human Expressiveness in Piano Performances with a Transformer Network

Georgy Fazekas, Jingjing Tang, Geraint A. Wiggins•Thu Jun 08 2023

A novel approach for reconstructing human expressiveness in piano performance with a multi-layer bi-directional Transformer encoder that integrates pianist identities to control the sampling process and explores the ability of the system to model variations in expressiveness for different pianists.

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ScorePerformer: Expressive Piano Performance Rendering With Fine-Grained Control

Ilya Borovik, Vladimir Viro•Sat Dec 31 2022

A tokenized representation of symbolic score and performance music, the Score Performance Music tuple (SPMuple), is designed, and a novel way to encode the local performance tempo in a local note time window is validated.

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DExter: Learning and Controlling Performance Expression with Diffusion Models

Huan Zhang, Shreyan Chowdhury, C. Cancino-Chac'on, Jinhua Liang, Simon Dixon, Gerhard Widmer•Thu Jun 20 2024

DExter is a new approach leveraging diffusion probabilistic models to render Western classical piano performances that enables the generation of interpretations guided by perceptually meaningful features by being jointly conditioned on score and perceptual-feature representations.

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