PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training
Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi, Wang, Chengying Gao

TL;DR
PianoBART is a large-scale pre-trained model based on BART that effectively learns musical structures for both symbolic piano music generation and understanding, achieving high-quality results.
Contribution
It introduces a multi-level object selection pre-training strategy for a unified model handling music generation and understanding.
Findings
Outperforms existing models in generating coherent piano music
Achieves superior accuracy in music understanding tasks
Effectively captures musical semantics during pre-training
Abstract
Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously. In this paper, we propose PianoBART, a pre-trained model that uses BART for both symbolic piano music generation and understanding. We devise a multi-level object selection strategy for different pre-training tasks of PianoBART, which can prevent information leakage or loss and enhance learning ability. The musical semantics captured in pre-training are fine-tuned for music generation and understanding tasks. Experiments demonstrate that PianoBART efficiently learns musical patterns and achieves outstanding performance in generating high-quality coherent pieces and comprehending music. Our code and supplementary material are available at…
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Taxonomy
TopicsMusic Technology and Sound Studies · Neuroscience and Music Perception · Music and Audio Processing
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Linear Layer · Multi-Head Attention · Residual Connection · Softmax · Byte Pair Encoding · Layer Normalization · Adam · Dropout
