In this paper we describe the implementation of an autonomous agent for live coding—the practice of creating art in real-time by writing computer code. The TidalCycles language (an extension of the strongly typed functional program-ming language Haskell) is used for the generation of new musical patterns. This is integrated as part of a system which allows automatic suggestion of the agent’s patterns to a live coder. We aim for this to be a co-creative system, using machine agents to explore not-yet conceptualised code sequences and support coders in asking new questions.

Digital sound synthesis can be described in terms of discrete operations applied to signals according to given criteria. The process of organising such operations can be subject to creative variation and generative algorithms. This article presents proposals for how a sound construction process can be thought of as a combination of synthesis and applied transformations. Operational spaces are examined and how process configuration and incompleteness can serve as creative models for synthesis. Properties of sound streams are also considered and how they can involve liveness and unpredictability. A software framework is finally introduced followed by a reflection on its use in creative practice.

Amid the current pandemic (COVID-19) disembodied presence has emerged as the new norm, revealing a world increasingly entangled with the technological and conceptual paradigm of the network. In this scenario, it is crucial to reconsider core axioms of the pervasive connectionist credo, acknowledging the empty space that a network subtends, the hollowness of its lattice, the ambivalence of its membership, the weave and web of its relational embodiment. This paper enquires on how such notions map onto the music/sound domain by drawing on aesthetics for a more lucid account, while also proposing a speculative approach to tracing inner layers of these networks, through three modalities: Artificial Neural Networks, Audible Ecosystems, and Networked Music Performance.

W.E.I.R.D. is a suite for piano solo combining the practice of data mining with a generative musical score. Data mining is deployed to extract tweets containing a set of keywords related to the sociological perspective on post-modernity proposed by Zygmunt Bauman. Such keywords are used to trigger the generation of a score written in common music notation, to be performed in real time by a soloist. Through the combination of data mining and generative scores, W.E.I.R.D. introduces the influence of a large sample of society on the stage, thus expanding the performance to emerging contemporary social dynamics. The first two movements of the suite are described from a technical and compositional perspective, and two demonstrative videos are presented outlining how the COVID-19 pandemic has influenced their implementation. Finally, a brief outlook of this work within the field of modern aesthetics and a series of possible developments for such compositional approach are proposed.