Automated manipulation of musical grammars to support episodic interactive experiences

Music is used to enhance the experience of participants and visitors in a range of settings including theatre, film, video games, installations and theme parks. These experiences may be interactive, contrastingly episodic and with variable duration. Hence, the musical accompaniment needs to be dynamic and to transition between contrasting music passages. In these contexts, computer generation of music may be necessary for practical reasons including distribution and cost. Automated and dynamic composition algorithms exist but are not well-suited to a highly interactive episodic context owing to transition-related problems including discontinuity, abruptness, extended repetitiveness and lack of musical granularity and musical form. Addressing these problems requires algorithms capable of reacting to participant behaviour and episodic change in order to generate formic music that is continuous and coherent during transitions. This thesis presents the Form-Aware Transitioning and Recovering Algorithm (FATRA) for realtime, adaptive, form-aware music generation to provide continuous musical accompaniment in episodic context. FATRA combines stochastic grammar adaptation and grammar merging in real time. The Form-Aware Transition Engine (FATE) implementation of FATRA estimates the time-occurrence of upcoming narrative transitions and generates a harmonic sequence as narrative accompaniment with a focus on coherent, form-aware music transitioning between music passages of contrasting character. Using FATE, FATRA has been evaluated in three perceptual user studies: An audioaugmented real museum experience, a computer-simulated museum experience and a music-focused online study detached from narrative. Music transitions of FATRA were benchmarked against common approaches of the video game industry, i.e. crossfading and direct transitions. The participants were overall content with the music of FATE during their experience. Transitions of FATE were significantly favoured against the crossfading benchmark and competitive against the direct transitions benchmark, without statistical significance for the latter comparison. In addition, technical evaluation demonstrated capabilities of FATRA including form generation, repetitiveness avoidance and style/form recovery in case of falsely predicted narrative transitions. Technical results along with perceptual preference and competitiveness against the benchmark approaches are deemed as positive and the structural advantages of FATRA, including form-aware transitioning, carry considerable potential for future research

Nonlinearity as a strategy for creating postmodern musical texts in the 1970-1990s

It is relevant in this research context to consider the ways of organizing the text, algorithms, and ideological principles of modeling the content components of a musical work. The purpose of the study is to establish strategies for the development of postmodernism in music, as well as non-linearity in music as a creative strategy of postmodernism. The study is devoted to fragmentation as perception and accumulation of information, a form of artistic experience reflected in musical creativity. Precedent phenomena that perform the function of meaning-making in the music of the last decades of the 20th century are determined by their symbolism, the possibility of mentonization and evaluation. Thus, in the musical text of postmodernism, the new work is incorporated into the figurative and expressive system in the space of fragmentary discourse, and the extratextual content of the musical text is formed. The research methodology is based on complex approaches. The main methods used in work are description, analysis, and synthesis, the method of intertextuality and the comparative-historical method were used to work with the material. The result of the work is the definition of new methods of constructing musical works of fragmented discourse, where an effective means is the selection of precedent phenomena, an individualistic vision of the audience, and intertext

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

Methodology for the production and delivery of generative music for the personal listener : systems for realtime generative music production

This thesis will describe a system for the production of generative music through specific methodology, and provide an approach for the delivery of this material. The system and body of work will be targeted specifically at the personal listening audience. As the largest current consumer of music in all genres of music, this represents the largest and most applicable market to develop such a system for. By considering how recorded media compares to concert performance, it is possible to ascertain which attributes of performance may be translated to a generative media. In addition, an outline of how fixed media has changed how people listen to music directly will be considered. By looking at these concepts an attempt is made to create a system which satisfies societies need for music which is not only commodified and easily approached, but also closes the qualitative gap between a static delivery medium and concert based output. This is approached within the context of contemporary classical music. Furthermore, by considering the development and fragmentation of the personal listening audience through technological developments, a methodology for the delivery of generative media to a range of devices will be investigated. A body of musical work will be created which attempts to realise these goals in a qualitative fashion. These works will span the development of the composition methodology, and the algorithmic methods covered. A conclusion based on the possibilities of each system with regard to its qualitative output will form the basis for evaluation. As this investigation is seated within the field of music, the musical output and composition methodology will be considered as the primary deciding factor of a system's feasibility. The contribution of this research to the field will be a methodology for the composition and production of algorithmic music in realtime, and a feasible method for the delivery of this music to a wide audience

Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research

Computational Systems for Music Improvisation

Computational music systems that afford improvised creative interaction in real time are often designed for a specific improviser and performance style. As such the field is diverse, fragmented and lacks a coherent framework. Through analysis of examples in the field we identify key areas of concern in the design of new systems, which we use as categories in the construction of a taxonomy. From our broad overview of the field we select significant examples to analyse in greater depth. This analysis serves to derive principles that may aid designers scaffold their work on existing innovation. We explore successful evaluation techniques from other fields and describe how they may be applied to iterative design processes for improvisational systems. We hope that by developing a more coherent design and evaluation process, we can support the next generation of improvisational music systems

Culturally sensitive strategies for automatic music prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-112).Music has been shown to form an essential part of the human experience-every known society engages in music. However, as universal as it may be, music has evolved into a variety of genres, peculiar to particular cultures. In fact people acquire musical skill, understanding, and appreciation specific to the music they have been exposed to. This process of enculturation builds mental structures that form the cognitive basis for musical expectation. In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. In listening experiments involving music synthesis a culture-specific design fares significantly better than a culture-agnostic one. Hence my findings support the importance of computational enculturation for automatic music prediction. Furthermore I suggest that in order to sustain and cultivate the diversity of musical traditions around the world it is indispensable that we design culturally sensitive music technology.by Mihir Sarkar.Ph.D

PnP Maxtools: Autonomous Parameter Control in MaxMSP Utilizing MIR Algorithms

This research presents a new approach to computer automation through the implementation of novel real-time music information retrieval algorithms developed for this project. It documents the development of the PnP.Maxtools package, a set of open source objects designed within the popular programming environment MaxMSP. The package is a set of pre/post processing filters, objective and subjective timbral descriptors, audio effects, and other objects that are designed to be used together to compose music or improvise without the use of external controllers or hardware. The PnP.Maxtools package objects are designed to be used quickly and easily using a `plug and play\u27 style with as few initial arguments needed as possible. The PnP.Maxtools package is designed to take incoming audio from a microphone, analyze it, and use the analysis to control an audio effect on the incoming signal in real-time. In this way, the audio content has a real musical and analogous relationship with the resulting musical transformations while the control parameters become more multifaceted and better able to serve the needs of artists. The term Reflexive Automation is presented that describes this unsupervised relationship between the content of the sound being analyzed and the analogous and automatic control over a specific musical parameter. A set of compositions are also presented that demonstrate ideal usage of the object categories for creating reflexive systems and achieving fully autonomous control over musical parameters