Supporting Composers to Retrieve Synthesizers by Exploring Network for DAW-based Music

Composers for Digital Audio Workstation (DAW)-based music must choose synthesizers for their music from a wide range of options. Existing DAW editors provide only text-based retrieval, where users have to retrieve synthesizers only by the tone’s name. Thus, users must be knowledgeable about the relationships between instruments’ names and acoustic features. This paper visualizes the synthesizer network based on the characteristics of the acoustic features. The visualized network lets users compare synthesizers relatively and helps them retrieve intended tones. The proposed system has functions for enlarging nodes by clicking and listening to sample sounds, bookmarking favorite tones, and search refinements. We believe that these functions enhance the discovery and efficient retrieval of synthesizers.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.09.373
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
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article

Supporting Composers to Retrieve Synthesizers by Exploring Network for DAW-based Music

Ryosuke Yamanishi, Miyu Yoneda
Procedia Computer Science
Music and Audio Processing
article

Supporting Composers to Retrieve Synthesizers by Exploring Network for DAW-based Music

Ryosuke Yamanishi, Miyu Yoneda
article en

Abstract

Composers for Digital Audio Workstation (DAW)-based music must choose synthesizers for their music from a wide range of options. Existing DAW editors provide only text-based retrieval, where users have to retrieve synthesizers only by the tone’s name. Thus, users must be knowledgeable about the relationships between instruments’ names and acoustic features. This paper visualizes the synthesizer network based on the characteristics of the acoustic features. The visualized network lets users compare synthesizers relatively and helps them retrieve intended tones. The proposed system has functions for enlarging nodes by clicking and listening to sample sounds, bookmarking favorite tones, and search refinements. We believe that these functions enhance the discovery and efficient retrieval of synthesizers.

Procedia Computer ScienceVol. 270
Kansai University (JP)
Openalex Percentile: Top 59%
Music and Audio Processing
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