Research and Design of Automatic Piano Accompaniment System Based on Figure Database

算法作曲是人工智能领域中一个研究方向，而为旋律配伴奏是其中一个十分具有挑战性和现实意义的研究分支。本文把旋律配和声看作是类似机器学习的一项任务，通过对于一系列样本的训练，构建了一个钢琴伴奏音型元结构数据库。随后，建立两个HMM模型来尝试着模拟作曲家为旋律配伴奏的思维过程，最终利用Viterbi算法为新的旋律乐段配钢琴伴奏。 文中，提出了“钢琴伴奏元结构”的概念，钢琴伴奏元结构由钢琴原始伴奏音型转换而来，元结构中每个音符的音高信息是一个变量，它随着应用环境的变化而不同，并且元结构中包含了能够表征一个和声状态的单拍子独立音群结构或单小节音群结构，以及涵盖1~3小节音乐片段中的模仿，模进创作技巧。...Algorithmic Composition is a research area of Artificial Intelligence, and Harmonizing Melody is one of its branches. Here we regard melody harmonization with piano accompaniment as a machine learning task. After a series of existing samples consisting of melody with piano accompaniment are inputted for training, we construct a database collecting the meta-structures of piano accompaniment figures...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302006115245

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Melody Harmonization

Vedci z oboru informačných technológií oddávna považovali hudbu za obzvlášť zaujímavé umenie. Pravdou je, že história hudby tvorenej počítačom je skoro tak dlhá ako história počítačovej vedy. Programy pre komponovanie, alebo tvorenie hudby" na rôznych úrovniach procesu kompozície boli vyvíjané už od 50tych rokov minulého storočia. Táto bakalárska práca uvádza hlavné prístupy v oblasti automatickej harmonizácie t.j. Problém produkovania hudobného aranžmá (nôt) z daných melódií, a sústreďuje sa na najpoužívanejšie techniky jeho riešenia. Hlavným cieľom tejto práce je návrh a implementácia softvérového systému pre automatickú harmonizáciu, ktorý by mal byť schopný naučiť sa pravidlá harmónie z databázy midi súborov. V tejto práci popíšem existujúce harmonizačné systémy a ďalej sa zameriam hlavne na princípy strojového učenia - teóriu a aplikáciu umelých neurónových sietí a ich použitie pre harmonizáciu.Computer scientists have long been considering music as a particularly interesting art Indeed, the history of computer music is almost as long as the history of computer science. Programs to compose music, or to make music" at various levels of the composition process have been designed since the 50s. This bachelor's thesis surveys the main approaches in the field of automatic harmonization, i.e. the problem of producing musical arrangements (scores) from given melodies, and focuses on the most widely used techniques to do so. The main goal of this paper is the issue of design and implementation of a software system for an automatic music harmonization which should learn the rules of harmony from the database of midi file. In the paper. In this thesis I describe existing systems for harmonization and furthermore I focus mainly on principles of machine learning - theory and application of Artificial Neural Networks and their use for harmonization.

Exploring the Use of Marimba in Nordoff Robbins Music Therapy with Children with Autism Spectrum Disorder

This study explored the use of the marimba in Nordoff Robbins music therapy with children with Autism Spectrum Disorder. Three clients each received two individual 30 to 45-minute music therapy sessions over the course of three weeks. After reviewing the sessions an inductive thematic analysis was competed in order to determine themes that occurred throughout the study. Five themes were discussed using clinical examples and musical transcriptions. These themes included: (a) sharing the marimba to strengthen the therapeutic relationship, (b) the use of marimba as an accompanying instrument, (c) rhythmic structuring, (d) the use of Nordoff Robbins repertoire on marimba, and (e) the use of mallet techniques to reflect emotional qualities of the music. The use of mallet techniques in addition to the timbre and range of the instrument allowed for the marimba to be used clinically to engage clients in musical interactions. The examples included in the study indicated that the musical qualities of the marimba can be used to engage clients with ASD. There is still limited research on the use of the marimba in music therapy

Selected Songs of Nadia Boulanger: Formal Analysis and Adaptation for Brass Chamber Music

Worth can be a difficult thing to understand and define, even under the best of circumstances. Nadia Boulanger is known to have told her teacher, Gabriel Fauré, that “if there is one thing of which I am certain, it is that my music is worthless.” As a teacher, Boulanger empowered her students to listen for the internal logic present in the pieces of art that she considered valuable. This document will apply Nadia Boulanger’s teachings to her compositions to demonstrate that her music does have worth. To that end, these analyses will compile data to discern the internal logic in her pieces. As a performer, Nadia Boulanger coupled her analytical beliefs to her performance practice; her concerts were shaped by and carefully crafted to present her analyses. Her formal classes included an informal ticket to hear her commentaries in action. Therefore, in addition to analytical validation, the author intends to demonstrate the value of Nadia Boulanger’s music through the arrangement and performance of her compositions as brass chamber music. as brass chamber music

System Research about Melody Composing for Lyrics

算法作曲是人工智能领域中一个重要的研究方向，而为歌词配主旋律是其中一个十分具有挑战性和现实意义的研究分支。本文把为歌词配主旋律看作是类似机器学习的一项任务，通过训练一系列经过风格分类的样本谱例，分别建立了多种不同风格的隐马尔科夫模型来试图模拟作曲家为不同风格的歌词配主旋律的思维过程，从而实现了各种风格的作曲机的构架。此外，在训练过程中收集乐汇元结构和乐段设计模板，为乐曲创作提供计算机可用的模板和素材。至此，为歌词配主旋律可被抽象为一个分层控制的过程，即系统根据用户指定的旋律创作风格选用相应的风格作曲机生成最终的乐曲。 文中提出了“乐汇元结构”的概念，该结构由乐汇原始旋律音型转换而来。通过乐汇...Composing Melody for the Lyrics is a challenging and meaningful branch of the Algorithmic Composition, which is a subsidiary research area of the Artificial Intelligence. In this paper, composing melody for the given lyrics was taken as a machine learning task, in which a series of samples with melody and lyrics are trained to construct a database with meta-structures of melody segment. After the ...学位：工学硕士院系专业：信息科学与技术学院智能科学与技术系_模式识别与智能系统学号：3152008115333

음악적 요소에 대한 조건부 생성의 개선에 관한 연구: 화음과 표현을 중심으로

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2023. 2. 이교구.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.음악적 요소를 조건부 생성하는 분야인 CGMC는 멜로디나 화음과 같은 음악의 일부분을 기반으로 나머지 부분을 생성하는 것을 목표로 한다. 이 분야는 음악적 요소 간 복잡한 관계를 탐구하는 데 용이하고, 음악을 만드는 데 어려움을 겪는 비전문가들을 도울 수 있다. 최근 연구들은 딥러닝 기술을 활용하여 CGMC 시스템의 성능을 높여왔다. 하지만, 이러한 연구들에는 아직 생성 품질과 제어가능성 측면에서 두 가지의 한계점이 있다. 먼저, 생성된 음악의 음악적 구조가 명확하지 않다. 또한, 아직 좁은 범위의 음악적 요소 및 테스크만이 유연한 제어의 대상으로서 탐구되었다. 이에 본 학위논문에서는 CGMC의 개선을 위해 위 두 가지의 한계점을 해결하고자 한다. 첫 번째로, 음악 구조를 이루는 음악적 위계를 직관적으로 모델링하는 데 집중하고자 한다. 본래 데이터와 음, 화음과 같은 음악적 단위 간 정렬 경로를 사용하여 모델이 음악적으로 의미있는 종속성을 명확하게 배울 수 있도록 한다. 두 번째로, 잠재 표상을 활용하여 새로운 음악적 요소들을 유연하게 제어하고자 한다. 특히 잠재 표상이 의도된 요소에 대해 풀리도록 훈련하기 위해서 비지도 혹은 자가지도 학습 프레임워크을 사용하여 잠재 표상을 제한하도록 한다. 본 학위논문에서는 CGMC 분야의 대표적인 두 테스크인 멜로디 하모나이제이션 및 표현적 연주 렌더링 테스크에 대해 위의 두 가지 방법론을 검증한다. 다양한 실험적 결과들을 통해 제안한 방법론이 CGMC 시스템의 음악적 창의성을 안정적인 생성 품질로 확장할 수 있다는 가능성을 시사한다.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 초 록 137박

The Benefits of Hybrid Picking in an Undergraduate Guitar Curriculum to Equip Students for a Diverse Performance Career

This qualitative study underscores a hybrid picking approach to the guitar curriculum at the undergraduate level to ensure better career opportunities for college graduates. While classical and commercial guitar styles vary in methodology, practice, and performance, many undergraduate guitarists may feel underprepared for professional career opportunities in the commercial realm based on the specific curriculum in the academic domain. In many undergraduate guitar curricula, guitarists must often choose between a classical or commercial approach to learning. Undergraduate guitarists may not be experienced in diverse guitar styles that could further a musical career if opportunities go beyond classical or jazz. The qualitative methodology of this study will compare standard undergraduate classical and jazz guitar curricula to some of the demands in commercial styles. Examples include traditional picking or strumming with a plectrum, guitar styles that employ hybrid picking, positioned scales with open strings, chord extensions, classical arpeggios and exercises, and comprehension of commercial techniques. While limited texts offer some concepts to address the significance of this problem, an undergraduate hybrid picking curriculum may be applicable for students desiring a full-time performance career in music, where many opportunities exclude standard classical and jazz guitar styles. The findings of this study could be valuable to both guitar instructors and students due to some limitations of standard guitar curricula found in many universities

Sunken II Chords and Inwardness: A Correspondence Complex in Robert Schumann’s \u3ci\u3eLiederjahr\u3c/i\u3e Songs

This dissertation advances a new approach to text-music relationships with a view to identifying and exploring a specific, recurring text-music relationship in Schuman’s Liederjahr songs. Chapter 1 proposes to update and restructure the taxonomy of possible text-music relationships. I argue that there are four categories of text-music relationships: two conjunctions, viz., correspondences and Widersprüche; and two disjunctions, viz., Gleichgültigkeiten and Eigenständigkeiten. I am principally interested in exploring how structures and tonal archetypes native to Schenkerian theory may function as musical metaphors for themes, ideas, and imagery in the text; a survey of extant literature reveals that Schenkerian analysts typically assert such correspondences using the linguistic formulations of metaphor (some variation on “M in the music IS T in the text”) or analogy (some variation on “m1 IS TO m2 in the music AS t1 IS TO t2 in the text”). A novel feature of my approach is that text-music relationships are formally expressed using a special symbolic notation. Symbolic definitions often collapse the distinction between metaphorical and analogical prose definitions, allowing analysts to compare seemingly disparate relationships on a level playing field. In response to Agawu’s criticism that musico-poetic analyses of Lieder are usually ad hoc and one-off (1999), I argue for the possibility of families of recurring text-music relationships, which I call text-music complexes (we may further distinguish between correspondence and Widerspruch complexes). Text-music complexes are archetypes for how some element in the text is mapped onto some element in the music for the creation of musico-poetic meaning in song; text-music relationships are their individual instantiations. As text-music relationships are to individual songs, text-music complexes are to sets of songs whose membership is circumscribed in some predetermined manner (e.g., the songs in a collection or cycle, songs by a specific composer, songs written during a specific historical era, etc.). Chapter 2 investigates the hermeneutical implications of passages where major V is followed, and prolonged, by minor II. The seeming breach of tonal syntax creates a perceived ebb in the tonal flow and gives the impression that V has somehow turned inward. By analogy to origami, I call this family of V prolongations a dominant sink fold; the Oberquintteiler here is called a sunken II chord. Because of their special voice-leading properties and inward affect, sunken IIs possess unique potential for creating text-music correspondences. The central claim of this dissertation is for the existence of a correspondence complex in Schumann’s Liederjahr songs that is bound up with a sense of inwardness. This correspondence complex (ℂ) takes sunken II as its musical element (), and any form of involution in the semantic dimension of the text as its textual element (); in symbolic notation, it is formally defined by the expression ℂInwardness = Sunken II ⚭ Inwardness. As evidence for an inwardness correspondence complex, Chapters 3–5 present analyses of three Schumann Lieder in which a sunken II in the music is meaningfully coordinated with some form of inward turn, introspection, or heightened subjectivity in the text. The three songs are “Der Nussbaum” (op. 25, no. 3), “Ich hab’ in mich gesogen” (op. 37, no. 5), and “Berg’ und Burgen schau’n herunter” (op. 24, no. 7)