An explicitly structured control model for exploring search space: chorale harmonisation in the style of J.S. Bach

In this research, we present our computational model which performs four part har-monisation in the style of J.S. Bach. Harmonising Bach chorales is a hard AI problem, comparable to natural language understanding. In our approach, we explore the issue of gaining control in an explicit way for the chorale harmonisation tasks. Generally, the control over the search space may be from both domain dependent and domain inde-pendent control knowledge. Our explicit control emphasises domain dependent control knowledge. The control gained from domain d ependent control enables us to map a clearer relationship between the control applied and its effects. Two examples of do-main dependent control are a plan of tasks to be done and heuristics stating properties of the domain. Examples of domain independent control are notions such as temperature values in an annealing method; mutation rates in Genetic Algorithms; and weights in Artificial Neural Networks.The appeal of the knowledge based approach lies in the accessibility to the control if required. Our system exploits this concept extensively. Control is explicitly expressed by weaving different atomic definitions {i.e. the rules, tests and measures) together with appropriate control primitives. Each expression constructed is called a control definition, which is hierarchical by nature.One drawback of the knowledge based approach is that, as the system grows bigger, the exploitation of the new added knowledge grows exponentially. This leads to an intractable search space. To reduce this intractability problem, we partially search the search space at the meta-level. This meta-level architecture reduces the complexity in the search space by exploiting search at the meta-level which has a smaller search space.The experiment shows that an explicitly structured control offers a greater flexibility in controlling the search space as it allows the control definitions to be manipulated and modified with great flexibility. This is a crucial clement in performing partial search over a big search space. As the control is allowed to be examined, the system also potentially supports elaborate explanations of the system activities and reflections at the meta-level

Artificial Intelligence in Music Education: A Critical Review

This paper reviews the principal approaches to using Artificial Intelligence in Music Education. Music is a challenging domain for Artificial Intelligence in Education (AI-ED) because music is, in general, an open-ended domain demanding creativity and problem-seeking on the part of learners and teachers. In addition, Artificial Intelligence theories of music are far from complete, and music education typically emphasises factors other than the communication of ‘knowledge’ to students. This paper reviews critically some of the principal problems and possibilities in a variety of AI-ED approaches to music education. Approaches considered include: Intelligent Tutoring Systems for Music; Music Logo Systems; Cognitive Support Frameworks that employ models of creativity; highly interactive interfaces that employ AI theories; AI-based music tools; and systems to support negotiation and reflection. A wide variety of existing music AI-ED systems are used to illustrate the key issues, techniques and methods associated with these approaches to AI-ED in Music

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Artificial intelligence, education and music : the use of artificial intelligence to encourage and facilitate music composition by novices

The goal of the research described in this thesis is to find ways of using artificial intelligence to encourage and facilitate music composition by musical novices, particularly those without traditional musical skills. Two complementary approaches are presented. We show how two recent cognitive theories of harmony can be used to design a new kind of direct manipulation tool for music, known as "Harmony Space", with the expressivity to allow novices to sketch, analyse, modify and compose harmonic sequences simply and clearly by moving two-dimensional patterns on a computer screen linker to a synthesizer. Harmony Space provides novices with a way of describing and controlling harmonic structures and relationships using a single, principled, uniform spatial metaphor at various musical levels; note level, interval level, chord level, harmonic succession level and key level. A prototype interface has been implemented to demonstrate the coherence and feasibility of the design. An investigation with a small number of subjects demonstrates that Harmony Space considerably reduces the prerequisites required for novices to learn about, sketch, analyse and experiment with harmony - activities that would normally be very difficult for them without considerable theoretical knowledge or instrumental skill. The second part of the thesis presents work towards a knowledge-based tutoring system to help novices using the interface to compose chord sequences. It is argued that traditional, remedial intelligent tutoring systems approaches are inadequate for tutoring in domains that require open-ended thinking. The foundation of a new approach is developed based on the exploration and transformation of case studies described in terms of chunks, styles and plans. This approach draws on a characterisation of creativity due to Johnson-Laird (1988). Programs have been implemented to illustrate the feasibility of key parts of the new approach

Modelling the perception and composition of Western musical harmony.

PhD ThesisHarmony is a fundamental structuring principle in Western music, determining how simultaneously occurring musical notes combine to form chords, and how successions of chords combine to form chord progressions. Harmony is interesting to psychologists because it unites many core features of auditory perception and cognition, such as pitch perception, auditory scene analysis, and statistical learning. A current challenge is to formalise our psychological understanding of harmony through computational modelling. Here we detail computational studies of three core dimensions of harmony: consonance, harmonic expectation, and voice leading. These studies develop and evaluate computational models of the psychoacoustic and cognitive processes involved in harmony perception, and quantitatively model how these processes contribute to music composition. Through these studies we examine long-standing issues in music psychology, such as the relative contributions of roughness and harmonicity to consonance perception, the roles of low-level psychoacoustic and high-level cognitive processes in harmony perception, and the probabilistic nature of harmonic expectation. We also develop cognitively informed computational models that are capable of both analysing existing music and generating new music, with potential applications in computational creativity, music informatics, and music psychology. This thesis is accompanied by a collection of open-source software packages that implement the models developed and evaluated here, which we hope will support future research into the psychological foundations of musical harmony.

Collaborating with the Behaving Machine: simple adaptive dynamical systems for generative and interactive music

Situated at the intersection of interactive computer music and generative art, this thesis is inspired by research in Artificial Life and Autonomous Robotics and applies some of the principles and methods of these fields in a practical music context. As such the project points toward a paradigm for computer music research and performance which comple- ments current mainstream approaches and develops upon existing creative applications of Artificial Life research. Many artists have adopted engineering techniques from the field of Artificial Life research as they seem to support a richer interactive experience with computers than is often achieved in digital interactive art. Moreover, the low level aspects of life which the research programme aims to model are often evident in these artistic appropriations in the form of bizarre and abstract but curiously familiar digital forms that somehow, despite their silicon make-up, appear to accord with biological convention. The initial aesthetic motivation for this project was very personal and stemmed from interests in adaptive systems and improvisation and a desire to unite the two. In sim- ple terms, I wanted to invite these synthetic critters up on stage and play with them. There has been some similar research in the musical domain, but this has focused on a very small selection of specific models and techniques which have been predominantly applied as compositional tools rather than for use in live generative music. This thesis considers the advantages of the Alife approach for contemporary computer musicians and offers specific examples of simple adaptive systems as components for both compo- sitional and performance tools. These models have been implemented in a range of generative and interactive works which are described here. These include generative sound installations, interactive instal- lations and a performance system for collaborative man-machine improvisation. Public response at exhibitions and concerts suggests that the approach taken here holds much promise

Neural ProbabilisticModels for Melody Prediction, Sequence Labelling and Classification

Data-driven sequence models have long played a role in the analysis and generation of musical information. Such models are of interest in computational musicology, computer-aided music composition, and tools for music education among other applications. This dissertation beginswith an experiment tomodel sequences of musical pitch in melodies with a class of purely data-driven predictive models collectively known as Connectionist models. It was demonstrated that a set of six such models could performon par with, or better than state-of-the-art n-gram models previously evaluated in an identical setting. A new model known as the Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM), was introduced in the process and found to outperform the rest of the models. A generalisation of this modelling task was also explored, and involved extending the set of musical features used as input by the models while still predicting pitch as before. The improvement in predictive performance which resulted from adding these new input features is encouraging for future work in this direction. Based on the above success of the RTDRBM, its application was extended to a non-musical sequence labelling task, namely Optical Character Recognition. This extension involved a modification to the model’s original prediction algorithm as a result of relaxing an assumption specific to the melody modelling task. The generalised model was evaluated on a benchmark dataset and compared against a set of 8 baseline models where it faired better than all of them. Furthermore, a theoretical extension to an existingmodel which was also employed in the above pitch prediction task - the Discriminative Restricted Boltzmann Machine (DRBM) - was proposed. This led to three new variants of the DRBM (which originally contained Logistic Sigmoid hidden layer activations), withHyperbolic Tangent, Binomial and Rectified Linear hidden layer activations respectively. The first two of these have been evaluated here on the benchmark MNIST dataset and shown to perform on par with the original DRBM

Modelling Motivic Processes in Music: A Mathematical Approach

This thesis proposes a new model for motivic analysis which, being based on the metaphor of a web or network and expanded using the mathematical field of graph theory, balances the polar concerns prevalent in analytical writing to date: those of static, out-of-time category membership and dynamic, in-time process. The concepts that constitute the model are presented in the third chapter, both as responses to a series of analytical observations (using the worked example of Beethoven’s Piano Sonata in F minor, Op. 2, No. 1), and as rigorously defined mathematical formalisms. The other chapters explore in further detail the disciplines and methodologies on which this model impinges, and serve both to motivate, and to reflect upon, its development. Chapter 1 asks what it means to make mathematical statements about music, and seeks to disentangle mathematics (as a tool or language) from science (as a method), arguing that music theory’s aims can be met by the former without presupposing its commonly assumed inextricability from the latter. Chapter 2 provides a thematic overview of the field of motivic theory and analysis, proposing four archetypal models that combine to underwrite much thought on the subject before outlining the problems inherent in a static account and the creative strategies that can be used to construct a dynamic account. Finally, Chapter 4 applies these strategies, together with Chapter 3’s model and the piece’s extensive existing scholarly literature, to the analysis of the first and last movements of Mahler’s Sixth Symphony. The central theme throughout – as it relates to mathematical modelling, music theory, and music analysis – is that of potential, invitation, openness, and dialogic engagement

Logic-based Modelling of Musical Harmony for Automatic Characterisation and Classification

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMusic like other online media is undergoing an information explosion. Massive online music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices offer vast storage capacities and cloud-based apps that can cater any music request. As Paul Lamere puts it7: “we can now have a virtually endless supply of music in our pocket. The ‘bottomless iPod’ will have as big an effect on how we listen to music as the original iPod had back in 2001. But with millions of songs to chose from, we will need help finding music that we want to hear [...]. We will need new tools that help us manage our listening experience.” Retrieval, organisation, recommendation, annotation and characterisation of musical data is precisely what the Music Information Retrieval (MIR) community has been working on for at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical fields such as Information Retrieval, Information Systems, Digital Resources and Digital Libraries but also from the publications presented at the first International Symposium on Music Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate, explore and make sense of music collections (Downie et al., 2009). That also includes analytical tools to suppor