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

AN APPROACH TO MACHINE DEVELOPMENT OF MUSICAL ONTOGENY

This Thesis pursues three main objectives: (i) to use computational modelling to explore how music is perceived, cognitively processed and created by human beings; (ii) to explore interactive musical systems as a method to model and achieve the transmission of musical influence in artificial worlds and between humans and machines; and (iii) to experiment with artificial and alternative developmental musical routes in order to observe the evolution of musical styles. In order to achieve these objectives, this Thesis introduces a new paradigm for the design of computer interactive musical systems called the Ontomemetical Model of Music Evolution - OMME, which includes the fields of musical ontogenesis and memetlcs. OMME-based systems are designed to artificially explore the evolution of music centred on human perceptive and cognitive faculties. The potential of the OMME is illustrated with two interactive musical systems, the Rhythmic Meme Generator (RGeme) and the Interactive Musical Environments (iMe). which have been tested in a series of laboratory experiments and live performances. The introduction to the OMME is preceded by an extensive and critical overview of the state of the art computer models that explore musical creativity and interactivity, in addition to a systematic exposition of the major issues involved in the design and implementation of these systems. This Thesis also proposes innovative solutions for (i) the representation of musical streams based on perceptive features, (ii) music segmentation, (iii) a memory-based music model, (iv) the measure of distance between musical styles, and (v) an impi*ovisation-based creative model

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

The construction and evaluation of statistical models of melodic structure in music perception and composition

The prevalent approach to developing cognitive models of music perception and composition is to construct systems of symbolic rules and constraints on the basis of extensive music-theoretic and music-analytic knowledge. The thesis proposed in this dissertation is that statistical models which acquire knowledge through the induction of regularities in corpora of existing music can, if examined with appropriate methodologies, provide significant insights into the cognitive processing involved in music perception and composition. This claim is examined in three stages. First, a number of statistical modelling techniques drawn from the fields of data compression, statistical language modelling and machine learning are subjected to empirical evaluation in the context of sequential prediction of pitch structure in unseen melodies. This investigation results in a collection of modelling strategies which together yield significant performance improvements over existing methods. In the second stage, these statistical systems are used to examine observed patterns of expectation collected in previous psychological research on melody perception. In contrast to previous accounts of this data, the results demonstrate that these patterns of expectation can be accounted for in terms of the induction of statistical regularities acquired through exposure to music. In the final stage of the present research, the statistical systems developed in the first stage are used to examine the intrinsic computational demands of the task of composing a stylistically successful melody The results suggest that the systems lack the degree of expressive power needed to consistently meet the demands of the task. In contrast to previous research, however, the methodological framework developed for the evaluation of computational models of composition enables a detailed empirical examination and comparison of such models which facilitates the identification and resolution of their weaknesses

Advances in Multiple Viewpoint Systems and Applications in Modelling Higher Order Musical Structure

PhDStatistical approaches are capable of underpinning strong models of musical structure, perception, and cognition. Multiple viewpoint systems are probabilistic models of sequential prediction that aim to capture the multidimensional aspects of a symbolic domain with predictions from multiple finite-context models combined in an information theoretically informed way. Information theory provides an important grounding for such models. In computational terms, information content is an empirical measure of compressibility for model evaluation, and entropy a powerful weighting system for combining predictions from multiple models. In perceptual terms, clear parallels can be drawn between information content and surprise, and entropy and certainty. In cognitive terms information theory underpins explanatory models of both musical representation and expectation. The thesis makes two broad contributions to the field of statistical modelling of music cognition: firstly, advancing the general understanding of multiple viewpoint systems, and, secondly, developing bottom-up, statistical learning methods capable of capturing higher order structure. In the first category, novel methods for predicting multiple basic attributes are empirically tested, significantly outperforming established methods, and refuting the assumption found in the literature that basic attributes are statistically independent from one another. Additionally, novel techniques for improving the prediction of derived viewpoints (viewpoints that abstract information away from whatever musical surface is under consideration) are introduced and analysed, and their relation with cognitive representations explored. Finally, the performance and suitability of an established algorithm that automatically constructs locally optimal multiple viewpoint systems is tested. In the second category, the current research brings together a number of existing statistical methods for segmentation and modelling musical surfaces with the aim of representing higher-order structure. A comprehensive review and empirical evaluation of these information theoretic segmentation methods is presented. Methods for labelling higher order segments, akin to layers of abstraction in a representation, are empirically evaluated and the cognitive implications explored. The architecture and performance of the models are assessed from cognitive and musicological perspectives.Media and Arts Technology programme, EPSRC Doctoral Training Centre EP/G03723X/1

Conditional preference networks: efficient dominance testing and learning

Modelling and reasoning about preference is necessary for applications such as recommendation and decision support systems. Such systems are becoming increasingly prevalent in all aspects of our daily lives as technology advances. Thus, preference representation is a wide area of interest within the Artificial Intelligence community. Conditional preference networks, or CP-nets, are one of the most popular models for representing a person's preference structure. In this thesis, we address two issues with this model that make it difficult to utilise in practice. First, answering dominance queries efficiently. Dominance queries ask for the relative preference between a given pair of outcomes. Such queries are natural and essential for effectively reasoning about a person's preferences. However, they are complex to answer given a CP-net representation of preference. Second, learning a person's CP-net from observational data. In order to utilise a CP-net representation of a person's preferences, we must first determine the correct model. As direct elicitation is not always possible or practical, we must be able to learn CP-nets passively from the data we can observe. We provide two distinct methods of improving dominance testing efficiency for CP-nets. The first utilises a quantitative representation of preference in order to prune the associated search tree. The second reduces the size of a dominance testing problem by preprocessing the CP-net. Both methods are shown experimentally to significantly improve dominance testing efficiency. Furthermore, both are shown to outperform existing methods. These techniques can be combined with one another, and with the existing methods, in order to further improve efficiency. We also introduce a new, score-based learning technique for CP-nets. Most existing work on CP-net learning uses pairwise outcome preferences as data. However, such preferences are often impossible to observe passively from user actions, particularly in online settings, where users typically choose from a variety of options. Contrastingly, our method assumes a history of user choices as data, which is observable in a wide variety of contexts. Experimental evaluation of this method finds that the learned CP-nets show high levels of agreement with the true preference structures and with previously unseen (future) data

Hierarchical music structure analysis, modeling and resynthesis : a dynamical systems and signal processing approach

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 153-156).The problem of creating generative music systems has been approached in different ways, each guided by different goals, aesthetics, beliefs and biases. These generative systems can be divided into two categories: the first is an ad hoc definition of the generative algorithms, the second is based on the idea of modeling and generalizing from preexistent music for the subsequent generation of new pieces. Most inductive models developed in the past have been probabilistic, while the majority of the deductive approaches have been rule based, some of them with very strong assumptions about music. In addition, almost all models have been discrete, most probably influenced by the discontinuous nature of traditional music notation. We approach the problem of inductive modeling of high level musical structures from a dynamical systems and signal processing perspective, focusing on motion per se independently of particular musical systems or styles. The point of departure is the construction of a state space that represents geometrically the motion characteristics of music. We address ways in which this state space can be modeled deterministically, as well as ways in which it can be transformed to generate new musical structures. Thus, in contrast to previous approaches to inductive music structure modeling, our models are continuous and mainly deterministic.(cont.) We also address the problem of extracting a hierarchical representation of music from the state space and how a hierarchical decomposition can become a second source of generalization.by Víctor Gabriel Adán.S.M

El fuzzy clustering y la similitud musical: aplicación a la composición asistida por ordenador

[ES] La composición musical asistida por ordenador es un área de conocimiento que tiene sus orígenes en la segunda mitad del siglo pasado. Durante sus más de sesenta años de existencia han aparecido numerosas propuestas para abordar el problema de la creatividad artificial aplicada al ámbito de la variación musical, la emulación de estilos, la escritura automatizada de contrapunto o la composición estocástica, entre muchos otros. En la presente memoria propondremos un nuevo método para la generación computacional de variaciones y transiciones a partir de material musical proporcionado por el compositor, ya sea de carácter melódico, rítmico, armónico o tímbrico. La originalidad de nuestro método radica en la construcción de nuevos algoritmos basados en las técnicas de agrupamiento difuso, capaces incorporar el orden de los elementos de los conjuntos de datos durante el proceso de partición. Para implementar computacionalmente estas técnicas hemos diseñado el software Mercury mediante el que realizaremos distintos experimentos cuyos resultados, en forma de transiciones musicales, ilustrarán la utilidad de nuestra propuesta. Completaremos la presente investigación con la composición de la obra Transiciones difusas, para cuarteto de cuerdas, adjunta como apéndice. La metodología propuesta implica formular una nueva medida de la disimilitud musical, aplicable de forma general a la comparación de dos secuencias numéricas cualesquiera, con las que se pueda representar cualquier tupla de atributos musicales. Es posible, por tanto, aplicar esta disimilitud sobre ámbitos más teóricos como los sistemas de afinación. Finalmente propondremos diversos métodos para estimar la compatibilidad entre un conjunto de notas y un sistema de afinación generando, en última instancia, transiciones entre diferentes sistemas.[CA] La composició musical assistida per ordinador és una àrea de coneixement que té els seus orígens a meitat del segle passat. Durant els seus més de seixanta anys d'existència han aparegut nombroses propostes per a abordar el problema de la creativitat artificial aplicada a l'àmbit de la generació de variacions, emulació d'estils, escriptura automatitzada de contrapunt i composició de música estocàstica, entre molts altres. En aquesta memòria proposarem un nou mètode per a crear variacions i transicions entre material musical preexistent, ja siga de caràcter melòdic, rítmic, harmònic o tímbric. L'originalitat del nostre mètode radica en la construcció d'algoritmes basats en la tècnica de fuzzy clustering, capaços de realitzar agrupaments en què es té en compte l'ordre dels elements dels conjunts de dades. Per a implementar aquestes tècniques, hem dissenyat el programari Mercury mitjançant el qual es realitzaran experiments amb transicions entre melodies, ritmes i seqüències harmòniques que il·lustraran la utilitat de la nostra proposta, i que culminaran amb la composició de l'obra Transicions difuses, adjunta com a apèndix. La metodologia proposada no només té conseqüències pràctiques, sinó que implica formular una nova mesura de la dissimilitud musical, aplicable de forma general a la comparació de qualsevol parell de seqüències numèriques, que puguen representar melodies, ritmes, harmonies o timbres. Un cop establert com valorar la dissimilitud, aquesta també pot aplicar-se a àmbits molt més teòrics, com són els sistemes d'afinació. Proposarem diversos mètodes per a estimar la compatibilitat entre un conjunt de notes i un sistema d'afinació i generar, en última instància, transicions entre dos sistemes d'afinació. Aquesta tasca pot facilitar la interpretació d'obres en un sistema d'afinació diferent d'aquell per al qual van ser concebudes, sempre que s'exigisca que el nivell de compatibilitat entre els dos sistemes siga acceptable.[EN] Computer-assisted composition is an area of knowledge that has its origins in the middle of the last century. During its more than sixty years of existence, numerous proposals have appeared to address the problem of artificial creativity applied to the field of generation of variations, emulation of styles, automated counterpoint writing, stochastic music composition, among many others. In this report we will propose a new method to create variations and transitions between pre-existing musical material, be it melodic, rhythmic, harmonic or timbre-related. The originality of our method lies in the construction of algorithms based on the technique of fuzzy clustering, capable of performing groupings in which the order of the elements of the data sets is taken into account. To implement these techniques, we designed the software Mercury through which experiments will be performed with transitions between melodies, rhythms and harmonic sequences that will illustrate the usefulness of our proposal, and that will culminate with the composition of the work Fuzzy Transitions, attached as an appendix. The proposed methodology not only has practical consequences, but also implies formulating a new measure of musical dissimilarity, applicable in a general way to the comparison of any pair of numerical sequences, which may represent melodies, rhythms, harmonies or timbres. Once established how to assess the dissimilarity, this can also be applied to much more theoretical areas, such as tuning systems. We will propose various methods to estimate the compatibility between a set of notes and an tuning system and, in the last instance, generate transitions between two tuning systems. This work can facilitate the interpretation of works in a tuning system different from that for which they were conceived, whenever it is required that the level of compatibility between both systems is acceptable.Martínez Rodríguez, BS. (2019). El fuzzy clustering y la similitud musical: aplicación a la composición asistida por ordenador [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/134056TESI