English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

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