Collage music : the development of a language of studio composition

Merged with duplicate record 10026.1/573 on 03.01.2017 by CS (TIS). Merged with duplicate record 10026.1/2630 on 28.02.2017 by CS (TIS)This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis is intended to amplify, support and provide historical and aesthetic contexts for the concerns which I have explored and developed in my creative practice as a composer. It is accompanied by three audio CDs containing six compositions which map the development of my language of studio music, together with a further two CDs containing earlier compositions and a sixth CD containing a musical reference compilation which supports the text. The thesis is divided into the following six sections: Introduction "a brief account of my background as a composer including a summary of the composition portfolios of my previous degrees, going on to discuss my first composition for this project, The Book (1999), which is submitted not as a portfolio piece but for reference only (CD 4) "a description of the subsequent ten compositions, only one of which is submitted, Summer Nights Dream (2001), which again is intended for reference only (CD 5) " the listing of a number of influential collage pieces according to the categorisation superimposition or juxtaposition which prefaces the history of collage music outlined in the next chapter. I Collage Music: History, Context and Influence "a positioning of collage music in both historical and cultural contexts; examples are drawn from both popular and classical musics including examples of contemporary studio-based music, in effect proposing a genre-crossing history of collage music which is currently undocumented " an examination of the ways in which the structure, pace and content in my studio music have been informed by comedy. This chapter is intended to be read in conjunction with the musical reference CD (6). 2 Composition and Computers: The Landscape of Studio Music " an exploration the various ways in which music technology has been an influence on the development of my compositional language "a brief survey of the field of algorithmic composition and a description of a suite of computer programs I designed in order to generate musical material "a discussion of a system of calculating modes which I devised in conjunction with these programs " an account of `large-scale phasing' including an examination of historical precedents in both the classical and popular music traditions for using this kind of generative system " an exploration of the notion of musical landscape as a means of pointing up a significant development in my approach to composition. 3 The Portfolio of Compositions: An Overview "a discussion of the development of my language of composition throughout the pieces in the portfolio "a grouping of my work into four approaches to form: generative landscape, episodic, rondo and fantasia " an examination of structure and gesture in my pieces. 4 Carnival of Light: An Account of my Compositional Process "a detailed account of the composition of one of the pieces in my portfolio in which I show how I have been inspired by texts, paintings, photographs and music in the creation of each section of my piece, hoping also to illuminate the thinking processes involved. Conclusion " an attempt to bring together the themes of each of the preceding sections, and to summarise the contribution I have made to the fields of studio composition and collage music " the introduction of the notion of altitude as a means of establishing a distinction between collage and non-collage music "a discussion of the issue of quotation in collage and a consideration of the relevance of collage music in contemporary culture.Dartington College of Art

AI 에밀리 하웰(Emily Howell)과 이아무스(Iamus)의 음악 연구

학위논문(석사) -- 서울대학교대학원 : 음악대학 음악과, 2021.8. 오희숙.This paper is a study that examines the history, the present and artistic possibilities of artificial intelligence composition that emerged with the development of scientific technology. Artificial intelligence composers such as Emily Howell and Iamus are currently attempting creative challenges to human composers by releasing albums and collaborating with human performers. In this context, the question arose, "Can artificial intelligence be truly creative?” Many scholars are divided over whether artificial intelligence is considered to be a subjective composer with original creativity or just a “tool” to improve human creativity.  Despite the passionate argument, current discussions have found little intrinsic analysis or attention to artificial intelligence music works in evaluating the creativity of artificial intelligence. Problematizing the situation, This paper studies the following questions: By what principles do AI composers compose their works? Is artificial intelligence's music truly creative? In other words, Can artificial intelligence music be evaluated as a Work of Art? Answering these questions, This paper tried to study the creative possibilities of artificial intelligence by paying attention to both the composition process and the outcome of artificial intelligence composition. This study is constituted into three-parts. Firstly, The Study investigated the developmental history of artificial intelligence, the musical background of Artificial Intelligence composition which is Algorithmic Composition, and current models of AI Composition which is broadly spread across the genre of popular music, and art music. In the study, the current aspects of artificial intelligence music can be divided into Transhumanistic models that play a role in helping human composition and Posthumanistic models that aim to develop computational creativity beyond human creativity.   Secondly, we selected and analyzed two models of artificial intelligence composition that could be discussed artistically and tried to discuss in-depth artificial intelligence composition. Selected research models, Emily Howell and Iamus, were able to evaluate the creativity of artificial intelligence composition in three aspects: the possibility of computational creativity, the artistical purpose of the models, and the choice of a genre they emulated to compose. Through the study of Howell and Iamus' composition principles and the analysis of the results produced through the process, this study was able to derive the following characteristics: First of all, Howell's "From Darkness, Light” composed of Prelude and Fuga revealed analytical features such as "Prolongation through Chromatic Progression” and "Reinterpretation of Subject-Response Relationships," and Iamus' piano solo "Colossus” and piano trio "Hello! World“ showed analytical features such as "absence of developmental narratives,” "loss of musical breathing,” and "repeating non-functional motifs.”  Lastly, based on the analytical characteristics of Howell and Iamus' composition, this study sought to identify the unique creativity of their music, and to answer the main question in the creative AI area, "Can artificial intelligence be creative?" Prior to the full-fledged discussion, we focused on the arguments of musician David Trippett and physicist Douglas Hofstadter. According to the computational creativity theory of psychologist Margaret A. Boden, Howell and Iamus' musical creativity were analyzed as "Inductive Associational Creativity” and "Randomized Creativity.”  As the first scholar to attempt research into computational creativity as the founder of computational psychology, Boden's theory of Computational Creativity opposed inspirational and mystical creativity and divided creativity into five categories. Among them, this study focused on Boden's concept of "Exploratory Creativity.” Howell's Exploratory Creativity was expressed through the Inductive Association Method, which Cope claimed was the source of Howell's creativity. The inductive association allowed Cope to change existing musical material into his own style on a variety of levels, from simple sound changes to re-forming the context of music, which led to Howell's music have its own stylistic "signatures" and psychologically creative results.  Iamus' musical creativity, which shows a link to the aesthetics of avant-garde music, could also be discussed in the field of exploratory creativity. Above all, Iamus music showed creativity in terms of randomness and unpredictability that Boden presented as a premise of computer creativity. Iamus' Randomized Creativity refers to the computational ability of the mechanism to randomly explore numerous possibilities in a very short period of time, and the gist of Iamus' randomized creativity was that humans could not predict the results produced by that calculation. In this context, researchers say that the complexity of Iamus music reveals "randomized creativity” as a result of computer-only computational skills beyond human cognitive capabilities, which has the possibility to go beyond the level of exploratory creativity to that of transformational creativity that can deviate from human musical rules. In conclusion, this study tried to answer the question of creative artificial intelligence, 'Can artificial intelligence be creative?' through the analysis of actual composition music of artificial intelligence. In a posthumous era in which technological advances have obscured the boundaries between humans and machines, technology and human coexistence are emphasized, and human-centered standards are required to re-evaluate, Artificial intelligence can be considered as the independent composer, not as a tool of human creativity. Therefore, This study might have an ultimate significance in that, as a peripheral presence in musicology, it examined the artistic possibilities of the "machine agent” in musicological discourse. keywords: AI Composition, Emily Howell, Iamus, Computational Creativity, Inductive Associational Creativity, Randomized Creativity Student Number : 2019-20918본 논문은 과학 기술의 발전에 따라 등장한 인공지능 작곡 음악의 역사와 현재, 예술적 가능성에 대해 고찰한 연구이다. 에밀리 하웰(Emily Howell), 이아무스(Iamus) 등의 인공지능 작곡가들은 현재 앨범을 발매하고, 인간 연주자와 함께 작업하는 등 인간 작곡가들에 대한 창의적 도전을 시도하고 있다. 이에 ‘인공지능이 진정으로 창의적일 수 있는가?’라는 질문이 대두되며 많은 학자들은 인공지능을 고유한 창의성을 가진 주체적 작곡가로 평가할 것인지, 인간의 창의성을 향상시키기 위한 도구에 불과한 것인지 각기 다른 평가를 내리며 컴퓨터의 창의성에 대한 논의가 분분하다. 그럼에도 불구하고 기존의 논의들은 인공지능의 창의성에 대한 평가에서 인공지능 음악 작품에 대한 내재적인 분석이나 주목은 거의 찾아볼 수 없었다. 이러한 문제의식에서 본 연구는 현재 인공지능 작곡가들이 어떤 원리로 작품을 작곡하고 있는가? 과연 인공지능이 작곡한 음악은 창의적인가? 즉, 인공지능 음악을 예술적으로 평가할 수 있을까?와 같은 질문을 던지며, 인공지능 작곡 음악의 작곡 과정과 결과물에 모두 주목하여 인공지능의 창의적 가능성에 대한 연구를 시도하고자 하였다. 크게 세 부분으로 진행된 본 연구에서는 먼저 논의의 배경이 되는 인공지능의 발전사와 인공지능 음악의 역사가 되는 알고리즘 작곡의 배경, 현재 대중음악, 예술음악을 넘나들며 다양한 장르에서 나타나는 인공지능 음악의 양상을 조사하였다. 현재 인공지능 음악의 양상은 인간의 작곡을 돕는 역할을 자처하는 트랜스휴머니즘 모델과 인간의 창의성을 뛰어 넘어 고유한 컴퓨터의 창의성을 드러내보이고자 하는 포스트휴머니즘 모델로 나눠볼 수 있었다. 두 번째로 현재 나타나고 있는 인공지능 작곡 음악 중 예술적으로 논의될 수 있을 만한 두 모델을 선정, 분석하여 인공지능 작곡 음악에 대한 심층적인 논의를 시도하였다. 중심 연구 모델인 에밀리 하웰(Emily Howell)과 이아무스(Iamus)는 ‘계산적 창의성(Computational Creativity)의 가능성’, ‘상업적인 목적이 아닌 예술적인 목적 아래 개발된 배경’, ‘현대음악 장르에의 도전’이라는 세 가지 측면에서 인공지능 작곡 음악의 창의성을 연구할 대상으로 평가할 수 있었다. 하웰과 이아무스의 공학적인 작곡 원리 연구와 그 과정을 거쳐 탄생한 결과물에 대한 분석을 통해 본 연구는 다음과 같은 특징을 도출할 수 있었다. 먼저 프렐류드와 푸가로 이루어진 하웰의 는 ‘반음계적 진행을 통한 연장’과 ‘주제-응답 5도 관계에 대한 재해석’이라는 분석적 특징이 나타났으며, 이아무스의 피아노 독주곡 와 피아노 트리오 는 ‘발전적 내러티브의 부재’, ‘쉼표의 사용을 통한 음악적 호흡의 상실’, ‘비기능적인 모티브 반복’이라는 분석적 특징을 보였다. 마지막으로 본 연구에서는 하웰과 이아무스의 음악의 작곡에 대한 분석적 특징을 토대로 이들의 음악에서 나타나는 고유한 창의성을 규명하고자 하며, 이를 통해 창의 영역의 주요 질문인 ‘인공지능은 창의적일 수 있는가?’에 답하고자 하였다. 본격적인 논의에 앞서 인공지능의 창의성에 대한 찬반 양론을 음악학자 데이비드 트리펫(David Trippett)과 물리학자 더글라스 호프스태터(Douglas Hofstadter)의 주장을 중심으로 살펴보았다. 이후 심리학자 마가렛 보덴(Margaret A. Boden)의 계산적 창의성 이론에 따라 하웰과 이아무스의 음악적 창의성을 ‘귀납적 연관의 창의성’(Inductive Associational Creativity)과 ‘무작위적 창의성’으로 분석하였다. 계산주의 심리학의 창시자로서 컴퓨터의 창의성에 대하여 진지한 연구를 시도한 첫 번째 학자로서 보덴의 계산적 창의성 이론은 영감주의적이고 신비적인 창의성에 대해 반대하며 창의성을 5가지 종류로 구분하였다. 그 중 본 연구는 보덴의 ‘탐구적 창의성’(Exploratory Creativity) 개념에 주목하였다. 하웰의 음악이 가지는 탐구적 창의성은 코프가 하웰의 창의성의 근원이라고 주장한 ‘귀납적 연관’(Inductive Associational Creativity) 방식을 통해 발현되었다. 귀납적 연관 방식은 코프가 컴퓨터에게 단순한 음의 변경부터 음악의 맥락을 재형성하는 것까지 다양한 층위에서 기존의 음악적 소재를 자신만의 스타일로 바꾸도록 하였던 방식으로, 이를 통해 하웰의 음악은 자신만의 양식적 ‘시그니처‘를 갖게 되었고, 심리적으로 창의적으로 보이는 결과물을 내놓았다. 아방가르드 음악의 미학과 연결고리를 보이는 이아무스의 음악적 창의성도 탐구적 창의성 영역에서 논의될 수 있었다. 무엇보다도 이아무스 음악은 보덴이 컴퓨터적 창의성의 전제로 제시한 무작위성과 예측불가능성의 측면에서 창의성을 보였다. 이아무스의 ‘무작위적 창의성’(Randomized Creativity)은 매우 짧은 시간 내에 수많은 가능성을 무작위적으로 탐색할 수 있는 메커니즘 상의 계산 능력을 말하며 인간은 그 계산으로 인해 산출된 결과물을 예측할 수 없다는 것이 이아무스의 무작위적 창의성의 요지였다. 이러한 맥락에서 연구자는 이아무스 음악의 복잡성은 인간의 인지 능력을 뛰어넘는 컴퓨터만의 계산 능력의 결과물로서 ‘무작위성 창의성’을 드러내며, 이는 탐구적 창의성을 넘어 인간의 음악적 규칙에서 벗어난 변형적 창의성의 가능성까지도 담지하고 있는 것이라 할 수 있다. 결론적으로 본 연구는 인공지능의 실제 작곡 음악을 통해 ‘인공지능은 창의적일 수 있는가?’라는 창의적 인공지능 영역의 문제에 답하고자 하였다. 현재 기술발전 의 도약으로 인간과 기계의 경계가 불분명해지고, 기술과 인간의 공존이 강조되며 인간중심주의적 기준들에 대한 재평가가 요구되는 포스트휴먼의 시대에서 본 연구는 인간의 ‘도구’가 아닌 또 다른 작곡적 주체로서 인공지능의 음악을 평가하고자 하였다. 이는 음악학 내에서의 주변적 존재로서 ‘기계’라는 대상의 예술적 가능성을 음악학적 담론 안에서 타진해보았다는 궁극적 의의가 존재할 것이다.I. 서론 1 1. 연구 배경 및 목적 1 2. 연구 방법 및 대상 5 II. 논의의 전제: 인공지능 작곡 음악의 등장 8 1. 인공지능의 역사 8 1) 1차 인공지능 붐(1960-1980) 8 2) 2차 인공지능 붐(1980-2000) 9 3) 3차 인공지능 붐(2010년 이후) 10 2. 인공지능 음악의 역사와 현재 12 1) 인공지능 음악의 역사: 알고리즘 작곡 12 2) 인공지능 음악의 현 양상 14 (1) 트랜스휴머니즘 모델 16 (2) 포스트휴머니즘 모델 20 III. 인공지능 작곡 모델 연구 25 1. 에밀리 하웰(Emily Howell, 2003) 25 1) 개발 배경 및 작곡 원리 25 2) 음반 어둠에서, 빛으로(From Darkness, Light, 2009) 32 3) 작품 분석: <어둠에서, 빛으로> (2003) 36 2. 이아무스(Iamus, 2010) 46 1) 개발 배경 및 작곡 원리 46 2) 음반 이아무스(Iamus, 2011) 50 3) 작품 분석: <콜로서스>, <헬로! 월드> 53 IV. 인공지능의 음악의 계산적 창의성 62 1. 인공지능의 창의성 논쟁: 인공지능은 창의적인가 63 1) 반대 입장: 음악학자 트리펫의 미적 판단 불가능성 63 2) 찬성 입장: 물리학자 호프스태터의 컴퓨터적 창의성 64 2. 인공지능 음악을 통해 본 인공지능의 창의적 가능성 66 1) 마가렛 보덴의 창의성에 대한 계산적 접근 66 2) 에밀리 하웰의 창의성: 귀납적 연관의 창의성 70 3) 이아무스의 창의성: 무작위적 창의성 74 V. 결론 77 참고문헌 79 Abstract 92석

Composition automatique de musique à l'aide de réseaux de neurones récurrents et de la structure métrique

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Live interactive music performance through the Internet

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (p. 96-97).by Charles Wei-Ting Tang.M.S

Hyperscore : a new approach to interactive, computer-generated music

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2001.Includes bibliographical references (leaves 78-81).This thesis discusses the design and implementation of Hyperscore, a computer-assisted composition system intended for users of all musical backgrounds. Hyperscore presents a unique graphical interface which takes input in the form of freehand drawing. The strokes in the drawing are mapped to structural and gestural elements in the music, allowing the user to describe the large scale-structure of a piece visually. Hyperscore's graphical notation also enables the depiction of musical ideas on a detailed level. Additional annotations around a main curve indicate the placement and emphasis of selected motives. These motives are short melodic fragments that are either composed by the user or selected from a set of pre-composed material. Changing qualitative aspects of the annotations such as texture and shape let the user alter different musical parameters. The ultimate goal of Hyperscore is to provide an intuitive, interactive graphical environment for creating and editing compositions.by Mary Farbood.S.M

Data-driven, memory-based computational models of human segmentation of musical melody

When listening to a piece of music, listeners often identify distinct sections or segments within the piece. Music segmentation is recognised as an important process in the abstraction of musical contents and researchers have attempted to explain how listeners perceive and identify the boundaries of these segments.The present study seeks the development of a system that is capable of performing melodic segmentation in an unsupervised way, by learning from non-annotated musical data. Probabilistic learning methods have been widely used to acquire regularities in large sets of data, with many successful applications in language and speech processing. Some of these applications have found their counterparts in music research and have been used for music prediction and generation, music retrieval or music analysis, but seldom to model perceptual and cognitive aspects of music listening.We present some preliminary experiments on melodic segmentation, which highlight the importance of memory and the role of learning in music listening. These experiments have motivated the development of a computational model for melodic segmentation based on a probabilistic learning paradigm.The model uses a Mixed-memory Markov Model to estimate sequence probabilities from pitch and time-based parametric descriptions of melodic data. We follow the assumption that listeners' perception of feature salience in melodies is strongly related to expectation. Moreover, we conjecture that outstanding entropy variations of certain melodic features coincide with segmentation boundaries as indicated by listeners.Model segmentation predictions are compared with results of a listening study on melodic segmentation carried out with real listeners. Overall results show that changes in prediction entropy along the pieces exhibit significant correspondence with the listeners' segmentation boundaries.Although the model relies only on information theoretic principles to make predictions on the location of segmentation boundaries, it was found that most predicted segments can be matched with boundaries of groupings usually attributed to Gestalt rules.These results question previous research supporting a separation between learningbased and innate bottom-up processes of melodic grouping, and suggesting that some of these latter processes can emerge from acquired regularities in melodic data