A Common Lisp client for SuperCollider, enabling algorithmic music composition and live coding within the Lisp environment.
cl-collider is a Common Lisp client library for SuperCollider, a platform for audio synthesis and algorithmic composition. It allows developers to programmatically generate and manipulate sound, create musical sequences, and perform live coding directly from a Lisp environment by interfacing with SuperCollider's sound server.
Common Lisp developers interested in audio programming, algorithmic music composition, live coding, and sound design. It's particularly suited for those already familiar with SuperCollider who want to work within a Lisp environment.
Developers choose cl-collider because it combines the interactive, expressive power of Common Lisp with SuperCollider's robust audio synthesis capabilities, enabling unique workflows for algorithmic music and live coding that aren't as easily achieved with other languages or tools.
A SuperCollider client for CommonLisp
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Provides full client access to SuperCollider's scsynth server for real-time sound generation, as demonstrated by server boot, synth control, and proxy examples in the README.
Uses Common Lisp syntax and macros like defsynth and proxy to define synths and sequences, making audio code expressive and integrated with Lisp workflows.
Designed for interactive performance with features like tempo-clock and real-time sequencing, backed by demo videos showing live coding sessions.
Enables non-real-time rendering to audio files for precise composition, as shown in the with-rendering macro that allows melody creation without live scheduling.
The README explicitly states it's an experimental project with possible API changes, which can break existing code and hinder long-term adoption.
Requires SuperCollider, Quicklisp, specific Lisp implementations, and additional tools like JACK on Linux or net-tools on Windows, making initial configuration cumbersome and error-prone.
Tutorial videos are deprecated with new ones promised, indicating current learning resources may be insufficient or misleading, relying on user familiarity with both ecosystems.