Beat induction is the perceptual and cognitive process by which humans listen to music and perceive a steady pulse. Computationally modelling beat induction is important for many Music Information Retrieval (MIR) methods and is in general an open problem, especially when processing expressive timing, e.g. tempo changes or rubato. A neuro-cognitive model has been proposed, the Gradient Frequency Neural Network (GFNN), which can model the perception of pulse and metre. GFNNs have been applied successfully to a range of ‘difficult’ music perception problems such as polyrhythms and syncopation. This thesis explores the use of GFNNs for expressive rhythm perception and modelling, addressing the current gap in knowledge for how to deal with varying tempo and expressive timing in automated and interactive music systems. The cannonical oscillators contained in a GFNN have entrainment properties, allowing phase shifts and resulting in changes to the observed frequencies. This makes them good candidates for solving the expressive timing problem. It is found that modelling a metrical perception with GFNNs can improve a machine learning music model. However, it is also discovered that GFNNs perform poorly when dealing with tempo changes in the stimulus. Therefore, a novel Adaptive Frequency Neural Network (AFNN) is introduced; extending the GFNN with a Hebbian learning rule on oscillator frequencies. Two new adaptive behaviours (attraction and elasticity) increase entrainment in the oscillators, and increase the computational efficiency of the model by allowing for a great reduction in the size of the network. The AFNN is evaluated over a series of experiments on sets of symbolic and audio rhythms both from the literature and created specifically for this research. Where previous work with GFNNs has focused on frequency and amplitude responses, this thesis considers phase information as critical for pulse perception. Evaluating the time-based output, it was found that AFNNs behave differently to GFNNs: responses to symbolic stimuli with both steady and varying pulses are significantly improved, and on audio data the AFNNs performance matches the GFNN, despite its lower density. The thesis argues that AFNNs could replace the linear filtering methods commonly used in beat tracking and tempo estimation systems, and lead to more accurate methods.

A portfolio of compositions for acoustic instruments, electronic resources alone, and for acoustic instruments and live electronics. The accompanying commentary describes the aesthetic and the context of these works, their approach to form, and traces the development of the techniques used in their composition. In particular, it discusses a variety of approaches to computer-aided algorithmic composition and stochastic processes for the generation of musical elements (e.g. chord sequences, rhythmic patterns, sound structures). Included in the commentary is a description of research into stochastic synthesis, and of the development of a personal implementation of Dynamic Stochastic Synthesis and Stochastic Concatenation of Dynamic Stochastic Synthesis in SuperCollider. LIST OF WORKS Surveillance (2011) for computer 15:00 Daisy (2011) for computer 9:40 Absorbed (2010) for 2 violas 9:00 My idea of fun (2010) for clarinet, percussion and viola 7:00 Brazil (2009) for computer 8:10 Spine (2008) for English horn, percussion, violin and double bass 8:30 "Sex, Drugs and Rock 'n Roll" was never meant to be like this (2007) for computer 9:40 Don't have any evidence (2007) for bass flute, English horn, bass clarinet, bassoon, percussion, piano, violin, viola, cello and double bass 9:30 Happy Birthday (2006/2007) for computer 8:00 Résistance (2006) for accordion 4:00 My life has been filled with terrible misfortune; most of which never happened (2004) for bass clarinet, violin, viola, cello, double bass and live electronics 9:00.

This commentary describes my recent approach to writing compositions for the ensemble performance of computer music. Drawing on experimental music and improvisation, I contend that such music is best considered in terms of people’s situated and relational interplay. The compositional and performative question that permeates this thesis is ‘what can we do, in this time and space, with these tools available to us?’. As themes of equality and egalitarian access underpin this work throughout, I highlight my engagement with Free Libre Open Source Software (FLOSS) ideology and community, reflecting on how this achieves my aims. I describe my writing of text score compositions, making use of the term bounded improvisation, whose purposeful requirements for indeterminate realisation extends most current computer-based performance practice. Though no single strand of this research is perhaps unusual by itself, such an assemblage as that outlined above (incorporating composition, computer coding and ensemble performance practice) is, when allied to an understanding of electronic and computer music praxis, currently an underdeveloped approach. Such an approach I have thus chosen to term free open computer music. I incorporate two further pre-existing conceptual formulations to present a framework for constructing, reflecting on, and developing my work in this field. Firstly flow or 'immersed experience' is useful to explicate difficult to capture aspects of instrumental engagement and ensemble performance. Secondly, this portfolio of scores aims to produce well-constructed situations, facilitating spaces of flow which contain within their environments the opportunity for an event to take place. I present the outcomes of my practice as place-forming tactics that catalyse something to do, but not what to do, in performative spaces such as those described above. Such intentions define my aims for composition. These theoretical concerns, together with an allied consideration of the underpinning themes highlighted above, is a useful framework for refection and evaluation of this work.

This dissertation describes a new method of computer performance of bowed string instruments (violin, viola, cello) using physical simulations and intelligent feedback control. Computer synthesis of music performed by bowed string instruments is a challenging problem. Unlike instruments whose notes originate with a single discrete excitation (e.g., piano, guitar, drum), bowed string instruments are controlled with a continuous stream of excitations (i.e. the bow scraping against the string). Most existing synthesis methods utilize recorded audio samples, which perform quite well for single-excitation instruments but not continuous-excitation instruments. This work improves the realism of synthesis of violin, viola, and cello sound by generating audio through modelling the physical behaviour of the instruments. A string's wave equation is decomposed into 40 modes of vibration, which can be acted upon by three forms of external force: A bow scraping against the string, a left-hand finger pressing down, and/or a right-hand finger plucking. The vibration of each string exerts force against the instrument bridge; these forces are summed and convolved with the instrument body impulse response to create the final audio output. In addition, right-hand haptic output is created from the force of the bow against the string. Physical constants from ten real instruments (five violins, two violas, and three cellos) were measured and used in these simulations. The physical modelling was implemented in a high-performance library capable of simulating audio on a desktop computer one hundred times faster than real-time. The program also generates animated video of the instruments being performed. To perform music with the physical models, a virtual musician interprets the musical score and generates actions which are then fed into the physical model. The resulting audio and haptic signals are examined with a support vector machine, which adjusts the bow force in order to establish and maintain a good timbre. This intelligent feedback control is trained with human input, but after the initial training is completed the virtual musician performs autonomously. A PID controller is used to adjust the position of the left-hand finger to correct any flaws in the pitch. Some performance parameters (initial bow force, force correction, and lifting factors) require an initial value for each string and musical dynamic; these are calibrated automatically using the previously-trained support vector machines. The timbre judgements are retained after each performance and are used to pre-emptively adjust bowing parameters to avoid or mitigate problematic timbre for future performances of the same music. The system is capable of playing sheet music with approximately the same ability level as a human music student after two years of training. Due to the number of instruments measured and the generality of the machine learning, music can be performed with ensembles of up to ten stringed instruments, each with a distinct timbre. This provides a baseline for future work in computer control and expressive music performance of virtual bowed string instruments.