89 research outputs found

    AI Methods in Algorithmic Composition: A Comprehensive Survey

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    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 Innovaciรณn, 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 Consejerรญa de Innovaciรณn y Ciencia de Andalucรญa

    Automated manipulation of musical grammars to support episodic interactive experiences

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    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

    A Functional Taxonomy of Music Generation Systems

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    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

    MediaScape: towards a video, music, and sound metacreation

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    We present a new media work, MediaScape, which is an initial foray into a fully interdisciplinary metacreativity. This paper defines metacreation, and we present examples of metacreative art within the fields of music, sound art, the history of generative narrative, and discuss the potential of the โ€œopen-documentaryโ€ as an immediate goal of metacreative video. Lastly, we describe MediaScape in detail, and present some future directions

    Harmonic Syntax of the Twelve-Bar Blues Form: A Corpus Study

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    THIS PAPER DESCRIBES THE CONSTRUCTION AND analysis of a corpus of harmonic progressions from 12-bar blues forms included in the jazz repertoire collection The Real Book. A novel method of coding and analyzing such corpus data is developed, with a notion of โ€˜โ€˜possible harmonic changeโ€™โ€™ derived from the corpus and logit mixed-effects regression models that describe the difference between actually occurring harmonic events and possible but non-occurring ones in terms of various sets of theoretical constructs. Models using different sets of constructs are compared using the Bayesian Information Criterion, which assesses the accuracy and efficiency of each model. The principal results are that: (1) transitional probabilities are better modeled using root-motion and chord-frequency information than they are using pairs of individual chords; (2) transitional probabilities are better described using a mixture model intermediate in complexity between a bigram and full trigram model; and (3) the difference between occurring and non-occurring chords is more efficiently modeled with a hierarchical, recursive context-free grammar than it is as a Markov chain. The results have implications for theories of harmony, composition, and cognition more generally

    The Spiral Model for Generative Harmony

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    Generative music is a broad and well-explored field, in which researchers have attempted various approaches at creating algorithmic models for the creation of music. Researchers may attempt to model the composition of melody, or of musical phrase structure, or, as is the focus of this paper, the harmonization of multiple voices. I use as the core of my model Elaine Chewโ€™s โ€œSpiral Arrayโ€, outlined in her 2000 thesis โ€œTowards a Mathematical Model Of Tonalityโ€. Chewโ€™s applications for this model were all analytical, gaining insights about human-composed pieces of music by running them through her model. My project is comprised of repurposing her framework as an agent-based model used for generation, analyzing the output of my model using some common metrics from the field of agent-based modeling, and providing commentary on its performance, and success as a music-making algorithm

    ์Œ์•…์  ์š”์†Œ์— ๋Œ€ํ•œ ์กฐ๊ฑด๋ถ€ ์ƒ์„ฑ์˜ ๊ฐœ์„ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ: ํ™”์Œ๊ณผ ํ‘œํ˜„์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Conditional generation of musical components (CGMC) creates a part of music based on partial musical components such as melody or chord. CGMC is beneficial for discovering complex relationships among musical attributes. It can also assist non-experts who face difficulties in making music. However, recent studies for CGMC are still facing two challenges in terms of generation quality and model controllability. First, the structure of the generated music is not robust. Second, only limited ranges of musical factors and tasks have been examined as targets for flexible control of generation. In this thesis, we aim to mitigate these two challenges to improve the CGMC systems. For musical structure, we focus on intuitive modeling of musical hierarchy to help the model explicitly learn musically meaningful dependency. To this end, we utilize alignment paths between the raw music data and the musical units such as notes or chords. For musical creativity, we facilitate smooth control of novel musical attributes using latent representations. We attempt to achieve disentangled representations of the intended factors by regularizing them with data-driven inductive bias. This thesis verifies the proposed approaches particularly in two representative CGMC tasks, melody harmonization and expressive performance rendering. A variety of experimental results show the possibility of the proposed approaches to expand musical creativity under stable generation quality.์Œ์•…์  ์š”์†Œ๋ฅผ ์กฐ๊ฑด๋ถ€ ์ƒ์„ฑํ•˜๋Š” ๋ถ„์•ผ์ธ CGMC๋Š” ๋ฉœ๋กœ๋””๋‚˜ ํ™”์Œ๊ณผ ๊ฐ™์€ ์Œ์•…์˜ ์ผ๋ถ€๋ถ„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‚˜๋จธ์ง€ ๋ถ€๋ถ„์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด ๋ถ„์•ผ๋Š” ์Œ์•…์  ์š”์†Œ ๊ฐ„ ๋ณต์žกํ•œ ๊ด€๊ณ„๋ฅผ ํƒ๊ตฌํ•˜๋Š” ๋ฐ ์šฉ์ดํ•˜๊ณ , ์Œ์•…์„ ๋งŒ๋“œ๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๋Š” ๋น„์ „๋ฌธ๊ฐ€๋“ค์„ ๋„์šธ ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ ์—ฐ๊ตฌ๋“ค์€ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ CGMC ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ๋†’์—ฌ์™”๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์—๋Š” ์•„์ง ์ƒ์„ฑ ํ’ˆ์งˆ๊ณผ ์ œ์–ด๊ฐ€๋Šฅ์„ฑ ์ธก๋ฉด์—์„œ ๋‘ ๊ฐ€์ง€์˜ ํ•œ๊ณ„์ ์ด ์žˆ๋‹ค. ๋จผ์ €, ์ƒ์„ฑ๋œ ์Œ์•…์˜ ์Œ์•…์  ๊ตฌ์กฐ๊ฐ€ ๋ช…ํ™•ํ•˜์ง€ ์•Š๋‹ค. ๋˜ํ•œ, ์•„์ง ์ข์€ ๋ฒ”์œ„์˜ ์Œ์•…์  ์š”์†Œ ๋ฐ ํ…Œ์Šคํฌ๋งŒ์ด ์œ ์—ฐํ•œ ์ œ์–ด์˜ ๋Œ€์ƒ์œผ๋กœ์„œ ํƒ๊ตฌ๋˜์—ˆ๋‹ค. ์ด์— ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” CGMC์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด ์œ„ ๋‘ ๊ฐ€์ง€์˜ ํ•œ๊ณ„์ ์„ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ์Œ์•… ๊ตฌ์กฐ๋ฅผ ์ด๋ฃจ๋Š” ์Œ์•…์  ์œ„๊ณ„๋ฅผ ์ง๊ด€์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐ ์ง‘์ค‘ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ๋ž˜ ๋ฐ์ดํ„ฐ์™€ ์Œ, ํ™”์Œ๊ณผ ๊ฐ™์€ ์Œ์•…์  ๋‹จ์œ„ ๊ฐ„ ์ •๋ ฌ ๊ฒฝ๋กœ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋ธ์ด ์Œ์•…์ ์œผ๋กœ ์˜๋ฏธ์žˆ๋Š” ์ข…์†์„ฑ์„ ๋ช…ํ™•ํ•˜๊ฒŒ ๋ฐฐ์šธ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์ž ์žฌ ํ‘œ์ƒ์„ ํ™œ์šฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ์Œ์•…์  ์š”์†Œ๋“ค์„ ์œ ์—ฐํ•˜๊ฒŒ ์ œ์–ดํ•˜๊ณ ์ž ํ•œ๋‹ค. ํŠนํžˆ ์ž ์žฌ ํ‘œ์ƒ์ด ์˜๋„๋œ ์š”์†Œ์— ๋Œ€ํ•ด ํ’€๋ฆฌ๋„๋ก ํ›ˆ๋ จํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋น„์ง€๋„ ํ˜น์€ ์ž๊ฐ€์ง€๋„ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ž ์žฌ ํ‘œ์ƒ์„ ์ œํ•œํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” CGMC ๋ถ„์•ผ์˜ ๋Œ€ํ‘œ์ ์ธ ๋‘ ํ…Œ์Šคํฌ์ธ ๋ฉœ๋กœ๋”” ํ•˜๋ชจ๋‚˜์ด์ œ์ด์…˜ ๋ฐ ํ‘œํ˜„์  ์—ฐ์ฃผ ๋ Œ๋”๋ง ํ…Œ์Šคํฌ์— ๋Œ€ํ•ด ์œ„์˜ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒ€์ฆํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ์‹คํ—˜์  ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋ก ์ด CGMC ์‹œ์Šคํ…œ์˜ ์Œ์•…์  ์ฐฝ์˜์„ฑ์„ ์•ˆ์ •์ ์ธ ์ƒ์„ฑ ํ’ˆ์งˆ๋กœ ํ™•์žฅํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค.Chapter 1 Introduction 1 1.1 Motivation 5 1.2 Definitions 8 1.3 Tasks of Interest 10 1.3.1 Generation Quality 10 1.3.2 Controllability 12 1.4 Approaches 13 1.4.1 Modeling Musical Hierarchy 14 1.4.2 Regularizing Latent Representations 16 1.4.3 Target Tasks 18 1.5 Outline of the Thesis 19 Chapter 2 Background 22 2.1 Music Generation Tasks 23 2.1.1 Melody Harmonization 23 2.1.2 Expressive Performance Rendering 25 2.2 Structure-enhanced Music Generation 27 2.2.1 Hierarchical Music Generation 27 2.2.2 Transformer-based Music Generation 28 2.3 Disentanglement Learning 29 2.3.1 Unsupervised Approaches 30 2.3.2 Supervised Approaches 30 2.3.3 Self-supervised Approaches 31 2.4 Controllable Music Generation 32 2.4.1 Score Generation 32 2.4.2 Performance Rendering 33 2.5 Summary 34 Chapter 3 Translating Melody to Chord: Structured and Flexible Harmonization of Melody with Transformer 36 3.1 Introduction 36 3.2 Proposed Methods 41 3.2.1 Standard Transformer Model (STHarm) 41 3.2.2 Variational Transformer Model (VTHarm) 44 3.2.3 Regularized Variational Transformer Model (rVTHarm) 46 3.2.4 Training Objectives 47 3.3 Experimental Settings 48 3.3.1 Datasets 49 3.3.2 Comparative Methods 50 3.3.3 Training 50 3.3.4 Metrics 51 3.4 Evaluation 56 3.4.1 Chord Coherence and Diversity 57 3.4.2 Harmonic Similarity to Human 59 3.4.3 Controlling Chord Complexity 60 3.4.4 Subjective Evaluation 62 3.4.5 Qualitative Results 67 3.4.6 Ablation Study 73 3.5 Conclusion and Future Work 74 Chapter 4 Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-supervised Learning 76 4.1 Introduction 76 4.2 Proposed Methods 79 4.2.1 Data Representation 79 4.2.2 Modeling Musical Hierarchy 80 4.2.3 Overall Network Architecture 81 4.2.4 Regularizing the Latent Variables 84 4.2.5 Overall Objective 86 4.3 Experimental Settings 87 4.3.1 Dataset and Implementation 87 4.3.2 Comparative Methods 88 4.4 Evaluation 88 4.4.1 Generation Quality 89 4.4.2 Disentangling Latent Representations 90 4.4.3 Controllability of Expressive Attributes 91 4.4.4 KL Divergence 93 4.4.5 Ablation Study 94 4.4.6 Subjective Evaluation 95 4.4.7 Qualitative Examples 97 4.4.8 Extent of Control 100 4.5 Conclusion 102 Chapter 5 Conclusion and Future Work 103 5.1 Conclusion 103 5.2 Future Work 106 5.2.1 Deeper Investigation of Controllable Factors 106 5.2.2 More Analysis of Qualitative Evaluation Results 107 5.2.3 Improving Diversity and Scale of Dataset 108 Bibliography 109 ์ดˆ ๋ก 137๋ฐ•

    16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

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    The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
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