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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Definitive embeddings remain a fundamental challenge of computationalmusicology for symbolic music in deep learning today. Analogous to naturallanguage, music can be modeled as a sequence of tokens. This motivates themajority of existing solutions to explore the utilization of word embeddingmodels to build music embeddings. However, music differs from natural languagesin two key aspects: (1) musical token is multi-faceted -- it comprises ofpitch, rhythm and dynamics information; and (2) musical context istwo-dimensional -- each musical token is dependent on both melodic and harmoniccontexts. In this work, we provide a comprehensive solution by proposing anovel framework named PiRhDy that integrates pitch, rhythm, and dynamicsinformation seamlessly. PiRhDy adopts a hierarchical strategy which can bedecomposed into two steps: (1) token (i.e., note event) modeling, whichseparately represents pitch, rhythm, and dynamics and integrates them into asingle token embedding; and (2) context modeling, which utilizes melodic andharmonic knowledge to train the token embedding. A thorough study was made oneach component and sub-strategy of PiRhDy. We further validate our embeddingsin three downstream tasks -- melody completion, accompaniment suggestion, andgenre classification. Results indicate a significant advancement of the neuralapproach towards symbolic music as well as PiRhDy's potential as a pretrainedtool for a broad range of symbolic music applications.

PiRhDy: Learning Pitch-, Rhythm-, and Dynamics-aware Embeddings for Symbolic Music