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.
Highlights
Computers, in their many incarnations, are nowadays ubiquitous at different points in most music creation and production workflows
The only exception is continuity, where all four algorithms score high. This shows that t-SNE and Uniform Manifold Approximation and Projection (UMAP) produce more clustered plots, which include only relevant neighbors for each point, whereas all four algorithms tend to preserve all neighbors in the feature space as neighbors in the reduced space
This opens up many possibilities for novel interfaces, by making use of other features available in the SuperCollider environment
Summary
In their many incarnations, are nowadays ubiquitous at different points in most music creation and production workflows One reason for this prevalence is the convenience of digital storage: compared with analog storage media such as magnetic tape, digital storage makes it much easier to access and manipulate large quantities of audio. Many software samplers feature skeuomorphic user interfaces that emulate with surprising detail the interface of early hardware samplers and sampling synthesizers Computer music languages such as Max, Pure Data or SuperCollider [1,2], mostly based on the Music N paradigm [3] Creative stages of music production are typically driven by musical intuitions and auditory cues In this context, dealing with labels and file systems can be disruptive, which hinders the use of large collections of sounds.
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