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

Data-based melody generation through multi-objective evolutionary computation

Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte [FPU fellowship AP2012-0939]; and the Spanish Ministerio de Economía y Competitividad project TIMuL supported by UE FEDER funds [No. TIN2013–48152–C2–1–R]

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

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

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

음악 장르로서의 문화 유물의 제품 진화에 대한 외부 요인의 영향

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2022. 8. 이정동.음악 진화는 중요한 문화적 현상입니다. 그것은 생물학적 및 기술적 진화와 같은 다른 유형의 진화와 관련이 있습니다. 그러나 제품 및 기술 진화의 개념 측면에서 음악 진화와 관련된 문화적 유물을 탐구하는 논문은 많지 않습니다. 또한 음악 진화에 영향을 미치는 외부 요인의 정량적 탐색에는 한계가 있습니다. 이 논문은 영국의 문화 유물로서의 장르의 진화를 주제 모델링 방법을 통해 검토함으로써 이러한 격차를 극복합니다. 1990 년대부터 2010 년대까지 세 가지 기간 동안 장르를 다루는 주제의 내용이 어떻게 변했는지 연구 할 수 있습니다.이 진화에 영향을 미치는 외부 요인은 공동 통합 테스트,그레인저-인과 관계 테스트 및 회귀 분석을 통해 확인되었습니다. 외부 요인은 문헌 검토에 기초하고 시계열 데이터의 형태를 갖는다. 연구를 통해 다음과 같은 결과가 발견되었습니다. 첫째,그것은 영국에서 음악 진화는 전문 장르와 지배적 인 장르로 바위의 형태를 가지고 있음을 발견했다. 둘째,장르 간의 차이의 감소를 의미하는 주제 내용 간의 차이가 감소한 것으로 확인되었습니다. 셋째,"여성의 힘"과 인터넷 사용자 수는 영국의 장르 진화에 영향을 미치고 관찰 된 기간에 장르 구성의 변화를 일으키는 극단적 인 요인이라는 것이 지적되었습니다. 얻어진 결과는 장르가 어떻게 변화 할 수 있고 음악의 진화가 미래에 어떻게 발생 하는지를 예측하기 위해 사용될 수 있습니다.Music evolution is an important cultural phenomenon. It is connected to other types of evolution such as biological and technological ones. However, not many papers explore cultural artefacts related to music evolution in terms of the concepts of product and technological evolution. Moreover, there are limitations in the quantitative exploration of external factors affecting music evolution. This paper overcomes these gaps by examining the evolution of genres as cultural artefacts in the UK via the method of topic modeling called LDA. LDA allows studying how the content of topics that cover genres have changed through three time periods from the 1990s to 2010s. The external factors that influence this evolution were identified through cointegration test, Granger-Causality test, and regression analysis. The external factors were based on the literature review and have a form of time series data. The following results were found through the research. Firstly, it was found out that music evolution in the UK has a form of rock as a dominant genre with specialized genres. Secondly, it was identified that the differences between contents of topics have reduced that imply a decrease of differences between genres. Thirdly, it was indicated that “Females’ power” and number of Internet users are the extremal factors that influences the evolution of genres in the UK and caused changes in genres’ compositions in the observed period of time. The obtained results might be used so as to predict how genres may change and music evolution occurs in the future.Contents vii List of Tables ix List of Figures x Chapter 1. Introduction 1 Chapter 2. Literature review 5 2.1 Overview of technological product evolution 5 2.1.1 The product life cycle 5 2.1.2 Dominant design 8 2.1.3 The product family evolution 9 2.2 Overview of music evolution 10 2.2.1 What is the music evolution? 11 2.2.2 The origin of music 14 2.3 Contemporary Music Evolution as a Social Phenomenon 21 2.3.1 Institutionalisation of Music---Case Study on Irish Music 21 2.3.2 Independent Music 23 2.4 Evolution of British Music---Case Studies on Multiculturalism 25 2.4.1 The Queen 26 2.4.2 British Bhangra Music 27 2.5 Psychological and ecological factors 29 2.5.1 Political factors 33 2.5.2 Demographic factors 36 2.5.3 Social and cultural factors 38 2.5.4 Technological factors 39 Chapter 3. Methodology 40 3.1 Latent Dirichlet Allocation (LDA) 40 3.2 Cointegration test, Granger-Causality test, and Linear Regression 46 Chapter 4. Data 48 4.1 Data for LDA analysis 48 4.2 Data for the regression analysis 50 Chapter 5. Empirical results 55 5.1 LDA results 55 5.2 Cointegration test, Granger-Causality test, and Linear Regression results 63 Chapter 6. Conclusion and discussion 66 Bibliography 70 Appendices 86 Abstract (Korean) 88석