From Musical Grammars to Music Cognition in the 1980s and 1990s: Highlights of the History of Computer-Assisted Music Analysis

While approaches that had already established historical precedents – computer-assisted analytical approaches drawing on statistics and information theory – developed further, many research projects conducted during the 1980s aimed at the development of new methods of computer-assisted music analysis. Some projects discovered new possibilities related to using computers to simulate human cognition and perception, drawing on cognitive musicology and Artificial Intelligence, areas that were themselves spurred on by new technical developments and by developments in computer program design. The 1990s ushered in revolutionary methods of music analysis, especially those drawing on Artificial Intelligence research. Some of these approaches started to focus on musical sound, rather than scores. They allowed music analysis to focus on how music is actually perceived. In some approaches, the analysis of music and of music cognition merged. This article provides an overview of computer-assisted music analysis of the 1980s and 1990s, as it relates to music cognition. Selected approaches are being discussed

Music Composition Using a Real-Time MRI Biofeedback System

The goal of this project was to describe a real time music composition through an fMRI based-biofeedback system that monitors the activity in the medial prefrontal cortex (MPFC) of the brain. Based on creativity and neuroscience research, it is suggested that increased MPFC activity is closely associated with creativity in jazz improvisation. [51

Non-Standard Sound Synthesis with Dynamic Models

Full version unavailable due to 3rd party copyright restrictions.This Thesis proposes three main objectives: (i) to provide the concept of a new generalized non-standard synthesis model that would provide the framework for incorporating other non-standard synthesis approaches; (ii) to explore dynamic sound modeling through the application of new non-standard synthesis techniques and procedures; and (iii) to experiment with dynamic sound synthesis for the creation of novel sound objects. In order to achieve these objectives, this Thesis introduces a new paradigm for non-standard synthesis that is based in the algorithmic assemblage of minute wave segments to form sound waveforms. This paradigm is called Extended Waveform Segment Synthesis (EWSS) and incorporates a hierarchy of algorithmic models for the generation of microsound structures. The concepts of EWSS are illustrated with the development and presentation of a novel non-standard synthesis system, the Dynamic Waveform Segment Synthesis (DWSS). DWSS features and combines a variety of algorithmic models for direct synthesis generation: list generation and permutation, tendency masks, trigonometric functions, stochastic functions, chaotic functions and grammars. The core mechanism of DWSS is based in an extended application of Cellular Automata. The potential of the synthetic capabilities of DWSS is explored in a series of Case Studies where a number of sound object were generated revealing (i) the capabilities of the system to generate sound morphologies belonging to other non-standard synthesis approaches and, (ii) the capabilities of the system of generating novel sound objects with dynamic morphologies. The introduction of EWSS and DWSS is preceded by an extensive and critical overview on the concepts of microsound synthesis, algorithmic composition, the two cultures of computer music, the heretical approach in composition, non- standard synthesis and sonic emergence along with the thorough examination of algorithmic models and their application in sound synthesis and electroacoustic composition. This Thesis also proposes (i) a new definition for “algorithmic composition”, (ii) the term “totalistic algorithmic composition”, and (iii) four discrete aspects of non-standard synthesis

Towards an Intelligent Learning Environment for Melody Composition Through Formalisation of Narmour's <i>Implication-Realisation Model</i>

This thesis describes research with the ultimate goal o f finding ways to use artificial intelligence (AI) to encourage and facilitate melody composition by musical novices. The work described in this thesis forms the groundwork for the development of intelligent learning environments for novice composers in this domain. There were two stages to the research. The first stage was the formalisation and testing of an existing analytical theory o f melody. This theory offers an explanation of how musical listeners break-up a melody into "chunks", and hear some notes as more important than others. The theory enables analysis of melodies in a hierarchical fashion. The formalisation process involved the implementation of a parser to create hierarchical analyses, and comparison of published analyses based on the theory with those created by the parser from the same melodies. From these results a critical evaluation of the analytical theory, and the parser, is presented. The second stage o f the research involved extending the parser with constraint-based generation techniques. One result is an AI tool (called MOTIVE), which can generate melodies given an analysis (from the parser, or constructed by the user) and a set of constraints to be applied at each hierarchical level. The features of the tool are presented, and a general architecture for an intelligent learning environment is proposed, within which MOTIVE would reside, which shows how the formalised analytical theory from the first part of the work could be used educationally by novice composers of melody

Abstract Representation of Music: A Type-Based Knowledge Representation Framework

The wholesale efficacy of computer-based music research is contingent on the sharing and reuse of information and analysis methods amongst researchers across the constituent disciplines. However, computer systems for the analysis and manipulation of musical data are generally not interoperable. Knowledge representation has been extensively used in the domain of music to harness the benefits of formal conceptual modelling combined with logic based automated inference. However, the available knowledge representation languages lack sufficient logical expressivity to support sophisticated musicological concepts. In this thesis we present a type-based framework for abstract representation of musical knowledge. The core of the framework is a multiple-hierarchical information model called a constituent structure, which accommodates diverse kinds of musical information. The framework includes a specification logic for expressing formal descriptions of the components of the representation. We give a formal specification for the framework in the Calculus of Inductive Constructions, an expressive logical language which lends itself to the abstract specification of data types and information structures. We give an implementation of our framework using Semantic Web ontologies and JavaScript. The ontologies capture the core structural aspects of the representation, while the JavaScript tools implement the functionality of the abstract specification. We describe how our framework supports three music analysis tasks: pattern search and discovery, paradigmatic analysis and hierarchical set-class analysis, detailing how constituent structures are used to represent both the input and output of these analyses including sophisticated structural annotations. We present a simple demonstrator application, built with the JavaScript tools, which performs simple analysis and visualisation of linked data documents structured by the ontologies. We conclude with a summary of the contributions of the thesis and a discussion of the type-based approach to knowledge representation, as well as a number of avenues for future work in this area

A metaphysical and neuropsychological assessment of musical tones to affect the brain, relax the mind and heal the body

It has been empirically established through many controlled studies that one of the most rewarding experiences known to humanity is listening to music, especially because it affects various parts of the brain and causes emotional arousal. The aim of this article is to do a succinct study on music and its effect on, especially, the nervous system, by referring to various empirical studies undertaken on the subject. The article, therefore, has a twofold purpose: (1) to show that throughout history, music has played a special role in various cultures and religions, especially as a healing tool and (2) to demonstrate that sound frequencies and vibrations found in music have the potential to realign the emotions of the nervous system and bring the body back into harmony by reducing stress. INTRADISCIPLINARY AND/OR INTERDISCIPLINARY IMPLICATIONS : The article’s challenge and purpose are to show that science and religion are not in conflict, but rather that together they can benefit both disciplines and make better sense of complicated topics, especially those related to how natural science and religion deal with the human body and health, and its relationship to the mind.Please note that the author has included information from his previously published article entitled ‘Sound: Conceivably the creative language of god, holding all of creation in concert’ published by Verbum et Ecclesia, University of Pretoria. http://www.ve.org. za/index.php/VE/article/view/485. This article is a supplement to it.Issachar Fund Sabbatical Writer’s Retreathttp://www.ve.org.zaam2018Dogmatics and Christian Ethic

A clustering-based approach to automatic harmonic analysis: an exploratory study of harmony and form in Mozart’s piano sonatas

We implement a novel approach to automatic harmonic analysis using a clustering method on pitch-class vectors (chroma vectors). The advantage of this method is its lack of top-down assumptions, allowing us to objectively validate the basic music theory premise of a chord lexicon consisting of triads and seventh chords, which is presumed by most research in automatic harmonic analysis. We use the discrete Fourier transform and hierarchical clustering to analyse features of the clustering solutions and illustrate associations between the features and the distribution of clusters over sections of the sonata forms. We also analyse the transition matrix, recovering elements of harmonic function theory.Published versio

Towards a general computational theory of musical structure

The General Computational Theory of Musical Structure (GCTMS) is a theory that may be employed to obtain a structural description (or set of descriptions) of a musical surface. This theory is based on general cognitive and logical principles, is independent of any specific musical style or idiom, and can be applied to any musical surface. The musical work is presented to GCTMS as a sequence of discrete symbolically represented events (e.g. notes) without higher-level structural elements (e.g. articulation marks, timesignature etc.)- although such information may be used to guide the analytic process. The aim of the application of the theory is to reach a structural description of the musical work that may be considered as &apos;plausible&apos; or &apos;permissible&apos; by a human music analyst. As styledependent knowledge is not embodied in the general theory, highly sophisticated analyses (similar to those an expert analyst may provide) are not expected. The theory gives, however, higher rating to descriptions that may be considered more reasonable or acceptable by human analysts and lower to descriptions that are less plausible