While computational models of human music making are a hot research topic, the human side of computer-based music making has been largely neglected. What are our cognitive processes like when we create musical algorithms, and when we compose and perform with them? Musical human–algorithm interaction involves embodied action, perception and interaction, and some kind of internalization of the algorithms in the performer’s mind. How does the cognitive relate to the physical here? Departing from the age-old mind–body problem, this chapter tries to answer these questions and review relevant research, drawing from a number of related fields, such as musical cognition, cognition and psychology of programming, embodied performance, and neurological research, as well as from the author’s personal experience as an artist working in the field.
What is time? This question has captivated philosophy again and again. The present chapter investigates how far algorithms involve temporality in a specific form, and why algorithmic music is a distinctive way of understanding time. Its orienting undercurrent is the idea that temporality, by its very nature, gives rise to conflictual perspectives that resist the attempt to be rendered in terms of a unified presence. These perspectives are coordinates of a tension field in which the algorithmic is necessarily embedded and invested, and which unfolds in algorithmic music. Drawing from a selection of examples and sources, the chapter leads through a series of such contradictions and touches upon a few interesting theories of time that have sprung from philosophy, music, and computer science, so as to actualize their mutual import.
This chapter explores algorithmic music and the software tools used to create it from the perspective of media that allow it to be distributed to mass audiences, such as smartphone apps, web-based experiences, and dedicated software packages. Different types of listener input and interaction for various algorithmic music formats are analysed, and examples of each are given. Advantages and disadvantages of various distribution platforms, both present and historic, are explored, and critical reaction to this wide body of work is also reviewed. Conclusions are drawn that the field is still relatively nascent, with advances in consumer technology being a main driver for innovation in this area of music distribution and creation.
Jan C. Schacher
Beginning with a brief historical overview of spatial audio and music practices, this chapter looks at principles of sound spatialization, algorithms for composing and rendering spatial sound and music, and different techniques of spatial source positioning and sound space manipulation. These operations include composing with abstract objects in a sound scene, creating compound sounds using source clusters, altering spatial characteristics by means of spectral sound decomposition, and the manipulation of artificial acoustic spaces. The chapter goes on to discuss practical issues of live spatialization and, through an example piece, the ways a number of different algorithms collaborate in the constitution of a generative audio-visual installation with surround audio and video. Finally, the challenges and pitfalls of using spatialization and some of the common reasons for failure are brought to attention.
This chapter explores the idea of central Javanese gamelan (also known as karawitan) as rule-based music, examining areas where algorithmic thinking can take place in both performance and composition. Different types of performance techniques are discussed, exploring the degree to which rules can be used to generate melodic content from a notated outline called the balungan (meaning ‘skeleton’ or ‘frame’ in Javanese). Several applications of algorithms in the contexts of ethnomusicology and composition are presented, with a focus on grammars and rewriting systems. This leads to a discussion of the author’s work with rule-based systems in composition and performance, including integration of computer parts in a live gamelan ensemble through augmented instruments. The chapter concludes with an overview of Pipilan: a piece of software developed in Max/MSP for computer-aided composition, which has also been used to facilitate audience participation in performance and installation settings.
Alex McLean and Roger T. Dean
This chapter discusses the contrasting creative experiences of the two editors of this volume on algorithmic music, two complementary people from very different generations and musical backgrounds. One is an experienced instrumentalist with conventional musical training, who has run an international intermedia creative ensemble for several decades. He came to algorithms so as to extend his musical practice, in part by listening. The other is primarily a computer musician, with more training in computation than instrumental performance, and who conversely came to music to extend his algorithmic practice, in part by listening. The contrast, described historically, embraces many aspects of algorithmic music, from live algorithms to live coding.
Andrew R. Brown
The chapter discusses how bringing music and computation together in the curriculum offers socially grounded contexts for the learning of digital expression and creativity. It explores how algorithms codify cultural knowledge, how programming can assist students in understanding and manipulating cultural norms, and how these can play a part in developing a student’s musicianship. In order to highlight how computational thinking extends music education and builds on interdisciplinary links, the chapter canvasses the challenges, and solutions, involved in learning through algorithmic music. Practical examples from informal and school-based educational contexts are included to illustrate how algorithmic music has been successfully integrated with established and emerging pedagogical approaches.
This chapter considers the topical competency of late eighteenth-century amateur players and listeners. Focus is on selected string quartets by Haydn, Mozart, and Pleyel. The analytical strategy is comparative, and therefore the analyses are limited to movements governed by clearly defined topics. The troping of learned and galant elements is the focus of discussion of three minuet movements, all of which incorporate contrapuntal techniques to varying structural and expressive ends. Parametric density is the focus of discussion of four chasse movements. In both sets of examples, issues considered include topical content and syntactical function, topical dissonance, and social and cultural associations.
This essay examines how undergraduate composition teachers assess growth in their students’ work, and shows how assessment frameworks (such as rubrics) can be useful for college composition students and professors alike. The essay presents interviews with university composition faculty to establish the assessment strategies generally used in lessons. Next, it looks critically at existing frameworks and assessment philosophies, considering their strengths and shortcomings for departments whose students are growing ever more diverse in musical style and voice. Finally, it considers the composer’s task of designing an assessment framework for his or her studio, including areas of concern and possible starting points for organization.
Contemporary music research and practice have leveraged advances in computing power by integrating computing devices into many aspects of music—from generative music to live coding. This efflorescence of musical practice, process, and product raises complex issues in audience reception. This chapter employs a comparative analysis in a longitudinal study designed to understand the psychological aspects of the audience reception of algorithmic music. It studies four compositions from the latter part of the twentieth century late, presented on fixed media to avoid variability in musical performance. Using a modified think-aloud protocol to collect data, this study shows that reception theory may be applied to the audience reception of algorithmic music using a cognitive-affective model to further understand the process of decoding of meaning. This study puts forth a robust methodology for future longitudinal and comparative research in the audience reception of music and makes recommendations for further research.