Designing a Highly Expressive Algorithmic Music Composition System for Non-Programmers

Algorithmic composition systems allow for the partial or total automation of music composition by formal, computational means. Typical algorithmic composition systems generate nondeterministic music, meaning that multiple musical outcomes can result from the same algorithm - consequently the output is generally different each time the algorithm runs

Choosers: designing a highly expressive algorithmic music composition system for non-programmers

We present an algorithmic composition system designed to be accessible to those with minimal programming skills and little musical training, while at the same time allowing the manipulation of detailed musical structures more rapidly and more fluidly than would normally be possible for such a user group. These requirements led us to devise non- standard programming abstractions as the basis for a novel graphical music programming language in which a single basic element permits indeterminism, parallelism, choice, multi-choice, recursion, weighting and looping. The system has general musical expressivity, but for simplicity here we focus on manipulating samples. The musical abstractions behind the system have been implemented as a set of SuperCollider classes to enable end-user testing of the graphical programming language via a Wizard of Oz prototyping methodology. The system is currently being tested with undergraduate Music Technology students who are typically neither programmers, nor traditional musicians

Choosers: The design and evaluation of a visual algorithmic music composition language for non-programmers

Algorithmic music composition involves specifying music in such a way that it is non-deterministic on playback, leading to music which has the potential to be different each time it is played. Current systems for algorithmic music composition typically require the user to have considerable programming skill and may require formal knowledge of music. However, much of the potential user population are music producers and musicians (some professional, but many amateur) with little or no programming experience and few formal musical skills. To investigate how this gap between tools and potential users might be better bridged we designed Choosers, a prototype algorithmic programming system centred around a new abstraction (of the same name) designed to allow non-programmers access to algorithmic music composition methods. Choosers provides a graphical notation that allows structural elements of key importance in algorithmic composition (such as sequencing, choice, multi-choice, weighting, looping and nesting) to be foregrounded in the notation in a way that is accessible to non-programmers. In order to test design assumptions a Wizard of Oz study was conducted in which seven pairs of undergraduate Music Technology students used Choosers to carry out a range of rudimentary algorithmic composition tasks. Feedback was gathered using the Programming Walkthrough method. All users were familiar with Digital Audio Workstations, and as a result they came with some relevant understanding, but also with some expectations that were not appropriate for algorithmic music work. Users were able to successfully make use of the mechanisms for choice, multi-choice, looping, and weighting after a brief training period. The ‘stop’ behaviour was not so easily understood and required additional input before users fully grasped it. Some users wanted an easier way to override algorithmic choices. These findings have been used to further refine the design of Choosers

Embracing openness: music technology pedagogy and curricula after the decline of the studio

Conference presentation; 'Learning On/With the Open Web'. 25th October 201

Choosers: A Visual Programming Language for Nondeterministic Music Composition by Non-Programmers

This thesis focuses on the design of Choosers, a prototype algorithmic programming system centred around a new abstraction (of the same name) designed to allow non-programmers access to nondeterministic music composition methods. Algorithmic composition typically involves structural elements such as indeterminism, parallelism, choice, multi-choice, nesting, weighting, and looping. There are powerful existing tools for manipulating these and other elements of music. However, while these systems give substantial compositional power to musicians who are also skilled programmers, many musicians who lack programming skills find these tools inaccessible and difficult to understand and use. This thesis presents the design and evaluation of a prototype visual programming language designed to allow structural elements of the kind involved in nondeterministic music composition to be readily visualised and manipulated, while making little or no demand on programming ability. Initially, a Cognitive Dimensions of Notations review of a representative selection of user interfaces for algorithmic composition software was conducted. The review led to a set of findings used to identify candidate design principles which were then tested via a series of design exercises. The findings from these design exercises led to the development of a new abstraction, the Chooser, via a series of iterative design cycles. Once a candidate design had been finalised it was evaluated with participants via two sets of programming walkthroughs, with the findings from each step used to refine the formalism. The final study used Choosers as a design probe through a series of interviews with domain experts in which manipulable compositions were introduced to prompt discussions on potential future implications for music computing education, music production, and music composition

A cognitive dimensions analysis of interaction design for algorithmic composition software

This paper presents an analysis of the user interfaces of a range of algorithmic music composition software using the Cognitive Dimensions of Notations as the main analysis tool. Findings include the following: much of the reviewed software exhibits a low viscosity and requires significant user knowledge. The use of metaphor (staff notation, music production hardware) introduces multiple levels of abstraction which the user has to understand in order to use effectively: some instances of close mapping reduce abstraction but require the user to do more work. Significant premature commitment is not conducive to music composition, and there are clear opportunities for the greater provisionality that a piece of structurally-aware music software could provide. Visibility and juxtaposability are frequently compromised by complex design. Patching software reduces the hard mental operations required of the user by making the signal flow clear, although graphical complexity can have a negative impact on role-expressiveness. Complexity leads to error-proneness in several instances, although there are some tools (such as error-checking and auto-completion) which seek to ameliorate the main problem

Toward meaningful algorithmic music-making for non-programmers

This is an accepted manuscript of an article published by PPIG in PPIG 2019, 30th Annual Workshop, 28th-30th August 2019. The accepted version of the publication may differ from the final published version.Algorithmic composition typically involves manipulating structural elements such as indeterminism, parallelism, choice, multi-choice, recursion, weighting, sequencing, timing, and looping. There exist powerful tools for these purposes, however, many musicians who are not expert programmers find such tools inaccessible and difficult to understand and use. By analysing a representative selection of user interfaces for algorithmic composition, through the use of the Cognitive Dimensions of Notations (CDN) and other analytical tools, we identified candidate design principles, and applied these principles to create and implement a new visual formalism, programming abstraction and execution model. The resulting visual programming language, Choosers, is designed to allow ready visualisation and manipulation of structural elements of the kind involved in algorithmic music composition, while making minimal demand on programming ability. Programming walkthroughs with novice users were used iteratively to refine and validate diverse aspects of the design. Currently, workshops with musical experts and teachers are being conducted to explore the value of the language for varied pragmatic purposes by expressing, manipulating and reflecting on diverse musical examples

An estimate of the number of tropical tree species

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher’s alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e. at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000–25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼4,500–6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa

An analysis of algorithmic composition interaction design with reference to Cognitive Dimensions

This paper presents an analysis, using Cognitive Dimensions (Blackwell & Green, 2003), of a representative selection of user interfaces for algorithmic composition software. Cognitive Dimensions are design principles for notations, user interfaces and programming language design, or from another viewpoint 'discussion tools' for designers (Green & Blackwell, 1998). For the purposes of this report, algorithmic composition software is software which generates music using computer algorithms, where the algorithms may be controlled by end users (who may variously be considered as composers or performers). For example, the algorithms may be created by the end user, or the user may provide data or parameter settings to pre-existing algorithms. Other kinds of end-user manipulation are also possible. A wide variety of algorithmic composition software is considered, including visual programming languages, text-oriented programming languages, and software which requires or allows data entry by the user. The paper considers a representative, rather than comprehensive, selection of software. The analysis also draws, where appropriate, on related discussion tools drawn from Crampton Smith (Moggridge, 2006), Cooper et al. (2007) and Rogers et al. (2011). Finally, the paper reflects on the compositional representation of time as a critical dimension of composition software that is not satisfactorily addressed by Cognitive Dimensions, or any of the other discussion tools