Inflorescence

No description supplie

Musical instrument mapping design with Echo State Networks

Echo State Networks (ESNs), a form of recurrent neural network developed in the field of Reservoir Computing, show significant potential for use as a tool in the design of mappings for digital musical instruments. They have, however, seldom been used in this area, so this paper explores their possible applications. This project contributes a new open source library, which was developed to allow ESNs to run in the Pure Data dataflow environment. Several use cases were explored, focusing on addressing current issues in mapping research. ESNs were found to work successfully in scenarios of pattern classification, multiparametric control, explorative mapping and the design of nonlinearities and uncontrol. 'Un-trained' behaviours are proposed, as augmentations to the conventional reservoir system that allow the player to introduce potentially interesting non-linearities and uncontrol into the reservoir. Interactive evolution style controls are proposed as strategies to help design these behaviours, which are otherwise dependent on arbitrary values and coarse global controls. A study on sound classification showed that ESNs could reliably differentiate between two drum sounds, and also generalise to other similar input. Following evaluation of the use cases, heuristics are proposed to aid the use of ESNs in computer music scenarios

Evaluating the Wiimote as a musical controller

The Nintendo Wiimote is growing in popularity with mu-sicians as a controller. This mode of use is an adaptationfrom its intended use as a game controller, and requiresevaluation of its functions in a musical context in orderto understand its possibilities and limits. Drawing on Hu-man Computer Interaction methodology, we assessed thecore musical applications of the Wiimote and designeda usability experiment to test them. 17 participants tookpart, performing musical tasks in four contexts: trigger-ing; precise and expressive continuous control; and ges-ture recognition. Interviews and empirical evidence wereutilised to probe the device’s limitations and its creativestrengths. This study should help potential users to planthe Wiimote’s employment in their projects, and should beuseful as a case study in HCI evaluation of musical con-trollers

Sample-level sound synthesis with recurrent neural networks and conceptors

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined

Multiparametric interfaces for fine-grained control of digital music

Digital technology provides a very powerful medium for musical creativity, and the way in which we interface and interact with computers has a huge bearing on our ability to realise our artistic aims. The standard input devices available for the control of digital music tools tend to afford a low quality of embodied control; they fail to realise our innate expressiveness and dexterity of motion. This thesis looks at ways of capturing more detailed and subtle motion for the control of computer music tools; it examines how this motion can be used to control music software, and evaluates musicians’ experience of using these systems. Two new musical controllers were created, based on a multiparametric paradigm where multiple, continuous, concurrent motion data streams are mapped to the control of musical parameters. The first controller, Phalanger, is a markerless video tracking system that enables the use of hand and finger motion for musical control. EchoFoam, the second system, is a malleable controller, operated through the manipulation of conductive foam. Both systems use machine learning techniques at the core of their functionality. These controllers are front ends to RECZ, a high-level mapping tool for multiparametric data streams. The development of these systems and the evaluation of musicians’ experience of their use constructs a detailed picture of multiparametric musical control. This work contributes to the developing intersection between the fields of computer music and human-computer interaction. The principal contributions are the two new musical controllers, and a set of guidelines for the design and use of multiparametric interfaces for the control of digital music. This work also acts as a case study of the application of HCI user experience evaluation methodology to musical interfaces. The results highlight important themes concerning multiparametric musical control. These include the use of metaphor and imagery, choreography and language creation, individual differences and uncontrol. They highlight how this style of interface can fit into the creative process, and advocate a pluralistic approach to the control of digital music tools where different input devices fit different creative scenarios

Towards new modes of collective musical expression through audio augmented reality

We investigate how audio augmented reality can engender new collective modes of musical expression in the context of a sound art installation, Listening Mirrors, exploring the creation of interactive sound environments for musicians and non-musicians alike. Listening Mirrors is designed to incorporate physical objects and computational systems for altering the acoustic environment, to enhance collective listening and challenge traditional musician-instrument performance. At a formative stage in exploring audio AR technology, we conducted an audience experience study investigating questions around the potential of audio AR in creating sound installation environments for collective musical expression. We collected interview evidence about the participants' experience and analysed the data with using a grounded theory approach. The results demonstrated that the technology has the potential to create immersive spaces where an audience can feel safe to experiment musically, and showed how AR can intervene in sound perception to instrumentalise an environment. The results also revealed caveats about the use of audio AR, mainly centred on social inhibition and seamlessness of experience, and finding a balance between mediated worlds to create space for interplay between the two

Continua: a resonator-feedback-cello duet for live coder and cellist

Continua is a duet for cellist and live coder, each playing a hybrid instrument based on the Halldorophone, an electroacoustic instrument in which electromagnetically-controlled feedback can be induced independently on each string. The physical interface ostensibly affords a traditional gestural performance practice, but one that is substantially altered by the nonlinear, generative feedback processes; the electronic interface allows for remote digital intervention and performance via live-coding, but is augmented by the physical interface

Listening mirrors: prototyping for a hybrid audio augmented reality installation

We introduce ongoing developments of Listening Mirrors, a sound art installation and live interface for musician and non-musician alike. The piece, in its construction and interaction design, investigates ways in which collective sonic expression can be made possible using Audio Augmented Reality technology (AAR) and acoustic mirrors, whilst asking how such environments promote collective sonic expression

Live coding machine learning and machine listening: a survey on the design of languages and environments for live coding

The MIMIC (Musically Intelligent Machines Interacting Creatively) project explores how the techniques of machine learning and machine listening can be communicated and implemented in simple terms for composers, instrument makers and performers. The potential for machine learning to support musical composition and performance is high, and with novel techniques in machine listening, we see emerging a technology that can shift from being instrumental to conversational and collaborative. By leveraging the internet as a live software ecosystem, the MIMIC project explores how such technology can best reach artists, and live up to its collaborative potential to fundamentally change creative practice in the field. The project involves creating a high level language that can be used for live coding, creative coding and quick prototyping. Implementing a language that interfaces with technically complex problems such as the design of machine learning neural networks or the temporal and spectral algorithms applied in machine listening is not a simple task, but we can build upon decades of research and practice in programming language design (Ko 2016), and computer music language design in particular, as well as a plethora of inventive new approaches in the design of live coding systems for music (Reina et al. 2019). The language and user interface design will build on recent research in creative coding and interactive machine learning, exemplified by the Rapid Mix project (Bernardo et. al., 2016, Zbyszynski et. al., 2017). Machine learning continues to be at the forefront of new innovations in computer music, (e.g. new sound synthesis techniques in NSynth (Engel et. al. 2017) and WaveNet (van den Oord, 2016)); the language will seek to integrate models based around these new techniques into live coding performance, and also explore the efficacy of live coding as an approach to training and exploiting these systems for analysing and generating sound. Existing live coding systems and languages are often reported on, describing clever solutions as well as weaknesses, as given, for example, in accounts of the development of Tidal (McLean, 2014), Extramuros (Ogborn et. al, 2015) and Gibber (Roberts and Kuchera-Morin, 2012). Researchers are typically reflective and openly critical of their own systems when analysing them and often report on its design with wider implications (Aaron 2011; Sorensen 2018). However, they rarely speculate freely and uninhibitedly about possible solutions or alternative paths taken; the focus is typically on the system described. Before defining the design of our own system, we were therefore interested in opening up a channel where we could learn from other practitioners in language design, machine learning and machine listening. We created a survey that we sent out to relevant communities of practice - such as live coding, machine learning, machine listening, creative coding, deep learning - and asked open questions about how they might imagine a future system implemented, given the knowledge we have today. Below we report on the questionnaire and its findings