This article presents recent outcomes of the author's research on musical complex adaptive systems (CASs). The first part focuses on the concepts of adaptation and complexity within the framework of CASs and suggests a rigorous placing of the concepts within the musical domain. This analysis involves a distinction of the notions of context and information between the engineering field of information theory and the philosophical one of radical constructivism. I conclude this section by showing that, in this approach, information and context are mutually determining. Then, I introduce a technique related to the notion of evolvability in biology and genetic algorithms and that has significantly increased the complexity and long-term variety in music systems during autonomous evolutions. This technique distributes adaptation across higher levels and allows the system to reorganize the relationships among its agents and their structure circularly while interpreting and constructing its context. To conclude, an autonomous live performance piece from 2019–2020, “Constructing Realities (Homage to Heinz von Foerster),” which implements the theories mentioned above, is described, showing DSP processes and techniques that relate to evolvability, autopoiesis, fitness, and complexity through agent-based modeling. This article is accompanied by a companion article discussing the technical aspects of information processing algorithms, which are an essential part for the implementation of music CASs: “Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing.”

In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal : “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”

The use of feedback-based systems in the music domain dates back to the 1960s. Their applications span from music composition and sound organization to audio synthesis and processing, as the interest in feedback resulted both from theoretical reflection on cybernetics and system theory, and from practical experimentation on analog circuits. The advent of computers has made possible the implementation of complex theoretical systems in audio-domain oriented applications, in some sense bridging the gap between theory and practice in the analog domain, and further increasing the range of audio and musical applications of feedback systems. In this article we first sketch a minimal history of feedback in music; second, we briefly introduce feedback systems from a theoretical point of view; then we propose a set of features that characterize them from the perspective of music applications; finally, we propose a typology targeted at feedback systems used in the audio/musical domain and discuss some relevant examples.