Papers › BERT-like Pre-training for Symbolic Piano Music Classification Tasks

BERT-like Pre-training for Symbolic Piano Music Classification Tasks

12 Jul 2021arXiv:2107.05223archive 2025-07-28

Yi-Hui Chou, I-Chun Chen, Chin-Jui Chang, Joann Ching, Yi-Hsuan Yang

This article presents a benchmark study of symbolic piano music classification using the masked language modelling approach of the Bidirectional Encoder Representations from Transformers (BERT). Specifically, we consider two types of MIDI data: MIDI scores, which are musical scores rendered directly into MIDI with no dynamics and precisely aligned with the metrical grid notated by its composer and MIDI performances, which are MIDI encodings of human performances of musical scoresheets. With five public-domain datasets of single-track piano MIDI files, we pre-train two 12-layer Transformer models using the BERT approach, one for MIDI scores and the other for MIDI performances, and fine-tune them for four downstream classification tasks. These include two note-level classification tasks (melody extraction and velocity prediction) and two sequence-level classification tasks (style classification and emotion classification). Our evaluation shows that the BERT approach leads to higher classification accuracy than recurrent neural network (RNN)-based baselines.

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wazenmai/MIDI-BERT officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Classification Emotion Classification Language Modelling Melody Extraction Music Classification

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Absolute Position Encodings Adam Attention Attention Dropout BERT BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Weight Decay WordPiece

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