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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern keyboards allow a musician to play multiple instruments at the sametime by assigning zones -- fixed pitch ranges of the keyboard -- to differentinstruments. In this paper, we aim to further extend this idea and examine thefeasibility of automatic instrumentation -- dynamically assigning instrumentsto notes in solo music during performance. In addition to the online,real-time-capable setting for performative use cases, automatic instrumentationcan also find applications in assistive composing tools in an offline setting.Due to the lack of paired data of original solo music and their fullarrangements, we approach automatic instrumentation by learning to separateparts (e.g., voices, instruments and tracks) from their mixture in symbolicmultitrack music, assuming that the mixture is to be played on a keyboard. Weframe the task of part separation as a sequential multi-class classificationproblem and adopt machine learning to map sequences of notes into sequences ofpart labels. To examine the effectiveness of our proposed models, we conduct acomprehensive empirical evaluation over four diverse datasets of differentgenres and ensembles -- Bach chorales, string quartets, game music and popmusic. Our experiments show that the proposed models outperform variousbaselines. We also demonstrate the potential for our proposed models to producealternative convincing instrumentations for an existing arrangement byseparating its mixture into parts. All source code and audio samples can befound at https://salu133445.github.io/arranger/ .

Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music