Ontology of music performance variation

Performance variation in rhythm determines the extent that humans perceive and feel the effect of rhythmic pulsation and music in general. In many cases, these rhythmic variations can be linked to percussive performance. Such percussive performance variations are often absent in current percussive rhythmic models. The purpose of this thesis is to present an interactive computer model, called the PD-103, that simulates the micro-variations in human percussive performance. This thesis makes three main contributions to existing knowledge: firstly, by formalising a new method for modelling percussive performance; secondly, by developing a new compositional software tool called the PD-103 that models human percussive performance, and finally, by creating a portfolio of different musical styles to demonstrate the capabilities of the software. A large database of recorded samples are classified into zones based upon the vibrational characteristics of the instruments, to model timbral variation in human percussive performance. The degree of timbral variation is governed by principles of biomechanics and human percussive performance. A fuzzy logic algorithm is applied to analyse current and first-order sample selection in order to formulate an ontological description of music performance variation. Asynchrony values were extracted from recorded performances of three different performance skill levels to create \timing fingerprints" which characterise unique features to each percussionist. The PD-103 uses real performance timing data to determine asynchrony values for each synthesised note. The spectral content of the sample database forms a three-dimensional loudness/timbre space, intersecting instrumental behaviour with music composition. The reparameterisation of the sample database, following the analysis of loudness, spectral flatness, and spectral centroid, provides an opportunity to explore the timbral variations inherent in percussion instruments, to creatively explore dimensions of timbre. The PD-103 was used to create a music portfolio exploring different rhythmic possibilities with a focus on meso-periodic rhythms common to parts of West Africa, jazz drumming, and electroacoustic music. The portfolio also includes new timbral percussive works based on spectral features and demonstrates the central aim of this thesis, which is the creation of a new compositional software tool that integrates human percussive performance and subsequently extends this model to different genres of music

Oblique Strategies

Geers reflects on the process to distinguish distinct musical style in composition through content and form

Mapping Textile Patterns into Sonic Experience

This portfolio contains seven works for a variety of ensembles and explores a number of distinct approaches of mapping textile patterns into musical parameters, incorporating various compositional techniques, such as microtonality, minimalism, serialism, and stochastic composition. The commentary examines the aesthetic links between the compositions through the exploration of the interaction of visuals and sonic art, analysing in detail the analogous features between them. It is not the intention of this commentary to inform the reader how to compose music that is derived from textile patterns. Instead, this commentary is to be viewed as a personal creative method, describing the concepts and techniques employed in the music. The commentary is divided into two parts. The first part aims to outline the general methods involved in the construction of textile patterns, focusing on possible relations with various musical parameters. The second part presents these ideas as realised in the practical setting of my compositional work, drawing on the diverse strands of my artistic practice

Canlı Ses İşleme Ve Geribesleme Kullanılarak Sempatik Titreşen Teller İle Sound Mass Tarzı Sesler Yaratmak

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Sosyal Bilimler Enstitüsü, 2015Thesis (Ph.D.) -- İstanbul Technical University, Institute of Social Sciences, 2015DoktoraPh.D

Generative Processes for Audification

Using the JavaSerial library, I present a generative method for the digital signal processing technique of audification. By analyzing multiple test instances produced by this method, I demonstrate that a generative audification process can be precise and easily controlled. The parameters tested in this experiment cause explicit, one-to-one changes in the resulting audio of each test instance

Algorithmic Compositional Methods and their Role in Genesis: A Multi-Functional Real-Time Computer Music System

Algorithmic procedures have been applied in computer music systems to generate compositional products using conventional musical formalism, extensions of such musical formalism and extra-musical disciplines such as mathematical models. This research investigates the applicability of such algorithmic methodologies for real-time musical composition, culminating in Genesis, a multi-functional real-time computer music system written for Mac OS X in the SuperCollider object-oriented programming language, and contained in the accompanying DVD. Through an extensive graphical user interface, Genesis offers musicians the opportunity to explore the application of the sonic features of real-time sound-objects to designated generative processes via different models of interaction such as unsupervised musical composition by Genesis and networked control of external Genesis instances. As a result of the applied interactive, generative and analytical methods, Genesis forms a unique compositional process, with a compositional product that reflects the character of its interactions between the sonic features of real-time sound-objects and its selected algorithmic procedures. Within this thesis, the technologies involved in algorithmic methodologies used for compositional processes, and the concepts that define their constructs are described, with consequent detailing of their selection and application in Genesis, with audio examples of algorithmic compositional methods demonstrated on the accompanying DVD. To demonstrate the real-time compositional abilities of Genesis, free explorations with instrumentalists, along with studio recordings of the compositional processes available in Genesis are presented in audiovisual examples contained in the accompanying DVD. The evaluation of the Genesis system’s capability to form a real-time compositional process, thereby maintaining real-time interaction between the sonic features of real-time sound objects and its selected algorithmic compositional methods, focuses on existing evaluation techniques founded in HCI and the qualitative issues such evaluation methods present. In terms of the compositional products generated by Genesis, the challenges in quantifying and qualifying its compositional outputs are identified, demonstrating the intricacies of assessing generative methods of compositional processes, and their impact on a resulting compositional product. The thesis concludes by considering further advances and applications of Genesis, and inviting further dissemination of the Genesis system and promotion of research into evaluative methods of generative techniques, with the hope that this may provide additional insight into the relative success of products generated by real-time algorithmic compositional processes

Generative rhythmic models

A system for generative rhythmic modeling is presented. The work aims to explore computational models of creativity, realizing them in a system designed for realtime generation of semi-improvisational music. This is envisioned as an attempt to develop musical intelligence in the context of structured improvisation, and by doing so to enable and encourage new forms of musical control and performance; the systems described in this work, already capable of realtime creation, have been designed with the explicit intention of embedding them in a variety of performance-based systems. A model of qaida, a solo tabla form, is presented, along with the results of an online survey comparing it to a professional tabla player's recording on dimensions of musicality, creativity, and novelty. The qaida model generates a bank of rhythmic variations by reordering subphrases. Selections from this bank are sequenced using a feature-based approach. An experimental extension into modeling layer- and loop-based forms of electronic music is presented, in which the initial modeling approach is generalized. Starting from a seed track, the layer-based model utilizes audio analysis techniques such as blind source separation and onset-based segmentation to generate layers which are shuffled and recombined to generate novel music in a manner analogous to the qaida model.M.S.Committee Chair: Chordia, Parag; Committee Member: Freeman, Jason; Committee Member: Weinberg, Gi

Automatic sound synthesizer programming: techniques and applications

The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis: tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a similar sound to the target. The techniques for achieving this that are investigated are genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis space explorer provides a user with a representation of the space of possible sounds that a synthesizer can produce and allows them to interactively explore this space. The applications of automatic sound synthesizer programming that are investigated include studio tools, an autonomous musical agent and a self-reprogramming drum machine. The research employs several methodologies: the development of novel software frameworks and tools, the examination of existing software at the source code and performance levels and user trials of the tools and software. The main contributions made are: a method for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme

Xenakis

Xenakis: His Life in Music is a full-length study of the influential contemporary composer Iannis Xenakis. Following the trajectory of Xenakis’s compositional development, James Harley, who studied with Xenakis, presents the works together with clear explanations of the technical and conceptual innovations that shaped them. Harley examines the relationship between the composer and two early influences: Messiaen and Le Corbusier. Particular attention is paid to analyzing works which were vital to the composer’s creative development, from early, unpublished works to the breakthrough pieces Metastasis and Pithoprakta, through the oft-discussed decade of formalization and the evolving styles of the succeeding three decades

Using conditional restricted Boltzmann machines to generate timbral music composition systems

Machine-learning models have been successfully applied to musical composition in a variety of forms, including audio classification, recognition, and synthesis. The capability of algorithms to learn complex musical elements allows composers to more deeply investigate the development of their aesthetic. Coupled with the history of interdisciplinary solutions found in computer music and system aesthetics, this capability has led to an exploration of the integration of machine learning and music composition. Composition systems that take advantage of this integration have the opportunity to be connected with algorithms in theory, application, and art. In my systems, conditional restricted Boltzmann machines (CRBM) synthesize musical timbre by learning autoregressive connections between the current output, an abstracted non-linear hidden feature layer, and past out- puts. This provides a creative space where composers can synthesize audio spectra in collaboration with machines, defining novel creative systems that explore compositional material in an abstract, non-linear paradigm. By implementing CRBMs in timbral-synthesis composition systems, I provide concrete support that such an integration advances art through the exploration of machine learning. I demonstrate this in a variety of audio synthesis experiments validating the capabilities of two algorithmic structures to synthesize and control timbre: a single layer conditional restricted Boltzmann machine (CRBM) and a single layer factored conditional restricted Boltzmann machine (FCRBM). I start by accurately synthesizing specific instrumental timbres and different musical pitches, demonstrating the aural capabilities of directly using the algorithms. I then build from these experiments, creating a set of compositional utilities that provide the composer with a rich pallet to provoke aesthetic introspection. These compositional utilities are then implemented in two music composition systems that synthesize and control timbre in application, where the algorithms themselves are designed and manipulated as a means to realize artwork. Through the creation of music composition systems that are able to accurately synthesize and control musical timbre, I demonstrate these models have the capability of provoking the aesthetic introspection of composers. The resulting systems show the power and potential of integrating music composition and machine learning, endorsing an interdisciplinary approach to the development of art and technology