Next Article in Journal
Effect Mechanism of Connection Joints in Fabricated Station Structures
Previous Article in Journal
Dynamic Response of Electro-Mechanical Properties of Cement-Based Piezoelectric Composites
Previous Article in Special Issue
Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
Article

A General Framework for Visualization of Sound Collections in Musical Interfaces

1
Centre for Research into New Music (CeReNeM), University of Huddersfield, Huddersfield HD1 3DH, UK
2
Music, Technology and Innovation (MTI2), De Montfort University, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Philippe Esling
Appl. Sci. 2021, 11(24), 11926; https://doi.org/10.3390/app112411926
Received: 12 November 2021 / Revised: 4 December 2021 / Accepted: 8 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue Advances in Computer Music)
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. View Full-Text
Keywords: data-driven music interfaces; dimensionality reduction; music visualization; sound collections; sound visualization; machine learning data-driven music interfaces; dimensionality reduction; music visualization; sound collections; sound visualization; machine learning
Show Figures

Figure 1

MDPI and ACS Style

Roma, G.; Xambó, A.; Green, O.; Tremblay, P.A. A General Framework for Visualization of Sound Collections in Musical Interfaces. Appl. Sci. 2021, 11, 11926. https://doi.org/10.3390/app112411926

AMA Style

Roma G, Xambó A, Green O, Tremblay PA. A General Framework for Visualization of Sound Collections in Musical Interfaces. Applied Sciences. 2021; 11(24):11926. https://doi.org/10.3390/app112411926

Chicago/Turabian Style

Roma, Gerard, Anna Xambó, Owen Green, and Pierre A. Tremblay. 2021. "A General Framework for Visualization of Sound Collections in Musical Interfaces" Applied Sciences 11, no. 24: 11926. https://doi.org/10.3390/app112411926

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop