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BERT-like Pre-training for Symbolic Piano Music Classification Tasks
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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.
Forward citations
Cited by 5 Pith papers
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Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Prediction
A music BERT with bounded token denoising and pianoroll prediction beats standard masked-language pre-training on a new 12-task symbolic music benchmark.
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Scaling Self-Supervised Representation Learning for Symbolic Piano Performance
Self-supervised pretraining on 60,000 hours of symbolic piano music produces a generative model and contrastive embeddings that beat leading baselines on continuation quality and several MIR classification benchmarks.
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From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training
A two-stage pre-train-then-fine-tune transformer with style adapters improves composer-style symbolic piano generation over from-scratch training and the NotaGen baseline.
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CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages
A contrastive learning framework (CLaMP 3) aligns three music modalities with multilingual text, enabling text-to-music retrieval, cross-lingual retrieval for unseen languages, and emergent cross-modal retrieval.
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PianoBind: A Multimodal Joint Embedding Model for Pop-piano Music
PianoBind, a trimodal audio-MIDI-text embedding model trained on piano data, beats general-purpose music embedding models on pop-piano text-to-music retrieval benchmarks.
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