A General Framework for Visualization of Sound Collections in Musical Interfaces

Date

2021-12-15

Advisors

Journal Title

Journal ISSN

ISSN

2076-3417

Volume Title

Publisher

MDPI

Type

Article

Peer reviewed

Yes

Abstract

While audio data play an increasingly central role in computer-based music production, interaction with large sound collections in most available music creation and production environments is very often still limited to scrolling long lists of file names. This paper describes a general framework for devising interactive applications based on the content-based visualization of sound collections. The proposed framework allows for a modular combination of different techniques for sound segmentation, analysis, and dimensionality reduction, using the reduced feature space for interactive applications. We analyze several prototypes presented in the literature and describe their limitations. We propose a more general framework that can be used flexibly to devise music creation interfaces. The proposed approach includes several novel contributions with respect to previously used pipelines, such as using unsupervised feature learning, content-based sound icons, and control of the output space layout. We present an implementation of the framework using the SuperCollider computer music language, and three example prototypes demonstrating its use for data-driven music interfaces. Our results demonstrate the potential of unsupervised machine learning and visualization for creative applications in computer music.

Description

open access article

Keywords

data-driven music interfaces, dimensionality reduction, music visualization, sound collections, sound visualization, machine learning

Citation

Roma, G.; Xambó, A.; Green, O.; Tremblay, P.A. (2021) A General Framework for Visualization of Sound Collections in Musical Interfaces. Applied Sciences, 11 (24), 11926

Rights

Research Institute

Music, Technology and Innovation - Institute for Sonic Creativity (MTI2)