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

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

특성 조절이 가능한 심층 신경망 기반의 구조적 멜로디 생성

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2021.8. 박종헌.This thesis aims to generate structural melodies using attribute controllable deep neural networks. The development of music-composing artificial intelligence can inspire professional composers and reduce the difficulty of creating and provide the public with the combination and utilization of music and various media content. For a melody generation model to function as a composer, it must control specific desired characteristics. The characteristics include quantifiable attributes, such as pitch level and rhythm density, and chords, which are essential elements that comprise modern popular (pop) music along with melodies. First, this thesis introduces a melody generation model that separately produces rhythm and pitch conditioned on chord progressions. The quantitative evaluation results demonstrate that the melodies produced by the proposed model have a distribution more similar to the dataset than other baseline models. Qualitative analysis reveals the presence of repetition and variation within the generated melodies. Using a subjective human listening test, we conclude that the model successfully produced new melodies that sound pleasant in rhythm and pitch. Four quantifiable attributes are considered: pitch level, pitch variety, rhythm density, and rhythm variety. We improve the previous study of training a variational autoencoder (VAE) and a discriminator in an adversarial manner to eliminate attribute information from the encoded latent variable. Rhythm and pitch VAEs are separately trained to control pitch-and rhythm-related attributes entirely independently. The experimental results indicate that though the ratio of the outputs belonging to the intended bin is not high, the model learned the relative order between the bins. Finally, a hierarchical song structure generation model is proposed. A sequence-to-sequence framework is adopted to capture the similar mood between two parts of the same song. The time axis is compressed by applying attention with different lengths of query and key to model the hierarchy of music. The concept of musical contrast is implemented by controlling attributes with relative bin information. The human evaluation results suggest the possibility of solving the problem of generating different structures of the same song with the sequence-to-sequence framework and reveal that the proposed model can create song structures with musical contrasts.본 논문은 특성 조절이 가능한 심층 신경망을 활용하여 구조적 멜로디를 생성하는 기법을 연구한다. 작곡을 돕는 인공지능의 개발은 전문 작곡가에게는 작곡의 영감을 주어 창작의 고통을 덜 수 있고, 일반 대중에게는 각종 미디어 콘텐츠의 종류와 양이 증가하는 추세에서 필요로 하는 음악을 제공해줌으로 인해 다른 미디어 매체와의 결합 및 활용을 증대할 수 있다. 작곡 인공지능의 수준이 인간 작곡가의 수준에 다다르기 위해서는 의도에 따른 특성 조절 작곡이 가능해야 한다. 여기서 말하는 특성이란 음의 높이나 리듬의 밀도와 같이 수치화 가능한 특성 뿐만 아니라, 멜로디와 함게 음악의 기본 구성 요소라고 볼 수 있는 코드 또한 포함한다. 기존에도 특성 조절이 가능한 음악 생성 모델이 제안되었으나 작곡가가 곡 전체의 구성을 염두에 두고 각 부분을 작곡하듯 긴 범위의 구조적 특징 및 음악적 대조가 고려된 특성 조절에 관한 연구는 많지 않다. 본 논문에서는 먼저 코드 조건부 멜로디 생성에 있어 리듬과 음높이를 각각 따로 생성하는 모델과 그 학습 방법을 제안한다. 정량적 평가의 결과는 제안한 기법이 다른 비교 모델들에 비해 그 생성 결과가 데이터셋과 더 유사한 분포를 나타내고 있음을 보여준다. 정성적 평가 결과 생성된 음악에서 적당한 반복과 변형이 확인되며, 사람이 듣기에 음정과 박자 모두 듣기 좋은 새로운 멜로디를 생성할 수 있다는 결론을 도출한다. 수치화 가능한 특성으로는 음의 높이, 음높이 변화, 리듬의 밀도, 리듬의 복잡도 네 가지 특성을 정의한다. 특성 조절이 가능한 변이형 오토인코더를 학습하기 잠재 변수로부터 특성 정보를 제외하는 판별기를 적대적으로 학습하는 기존 연구를 발전시켜, 음높이와 리듬 관련 특성을 완전히 독립적으로 조절할 수 있도록 두 개의 모델을 분리하여 학습한다. 각 구간마다 동일한 양의 데이터를 포함하도록 특성 값에 따라 구간을 나눈 후 학습한 결과, 생성 결과가 의도한 구간에 정확히 포함되는 비율은 높지 않지만 상관계수는 높게 나타난다. 마지막으로 앞의 두 연구의 결과를 활용하여, 음악적으로 비슷하면서도 서로 대조를 이루는 곡 구조 생성 기법을 제안한다. 시퀀스-투-시퀀스 문제 상황에서 좋은 성능을 보이는 트랜스포머 모델을 베이스라인으로 삼아 어텐션 매커니즘을 적용한다. 음악의 계층적 구조를 반영하기 위해 계층적 어텐션을 적용하며, 이 때 상대적 위치 임베딩을 효율적으로 계산하는 방법을 제시한다. 음악적 대조를 구현하기 위해 앞서 정의한 네 가지 특성 정보를 조절하도록 적대적 학습을 진행하고, 이 때 특성 정보는 정확한 구간 정보가 아닌 상대적 구간 비교 정보를 사용한다. 청취 실험 결과 같은 곡의 다른 구조를 생성하는 문제를 시퀀스-투-시퀀스 방법으로 해결할 수 있는 가능성을 제시하고, 제안된 기법을 통해 음악적 대조가 나타나는 곡 구조 생성이 가능하다는 점을 보여준다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Objectives 4 1.3 Thesis Outline 6 Chapter 2 Literature Review 7 2.1 Chord-conditioned Melody Generation 7 2.2 Attention Mechanism and Transformer 10 2.2.1 Attention Mechanism 10 2.2.2 Transformer 10 2.2.3 Relative Positional Embedding 12 2.2.4 Funnel-Transformer 14 2.3 Attribute Controllable Music Generation 16 Chapter 3 Problem Definition 17 3.1 Data Representation 17 3.1.1 Datasets 18 3.1.2 Preprocessing 19 3.2 Notation and Formulas 21 3.2.1 Chord-conditioned Melody Generation 21 3.2.2 Attribute Controllable Melody Generation 22 3.2.3 Song Structure Generation 22 3.2.4 Notation 22 Chapter 4 Chord-conditioned Melody Generation 24 4.1 Methodology 24 4.1.1 Model Architecture 24 4.1.2 Relative Positional Embedding 27 4.2 Training and Generation 29 4.2.1 Two-phase Training 30 4.2.2 Pitch-varied Rhythm Data 30 4.2.3 Generating Melodies 31 4.3 Experiments 32 4.3.1 Experiment Settings 32 4.3.2 Baseline Models 33 4.4 Evaluation Results 34 4.4.1 Quantitative Evaluation 34 4.4.2 Qualitative Evaluation 42 Chapter 5 Attribute Controllable Melody Generation 48 5.1 Attribute Definition 48 5.1.1 Pitch-Related Attributes 48 5.1.2 Rhythm-Related Attributes 49 5.2 Model Architecture 51 5.3 Experiments 54 5.3.1 Data Preprocessing 54 5.3.2 Training 56 5.4 Results 58 5.4.1 Quantitative Results 58 5.4.2 Output Examples 60 Chapter 6 Hierarchical Song Structure Generation 68 6.1 Baseline 69 6.2 Proposed Model 70 6.2.1 Relative Hierarchical Attention 70 6.2.2 Model Architecture 78 6.3 Experiments 84 6.3.1 Training and Generation 84 6.3.2 Human Evaluation 85 6.4 Evaluation Results 86 6.4.1 Control Success Ratio 86 6.4.2 Human Perception Ratio 86 6.4.3 Generated Samples 88 Chapter 7 Conclusion 104 7.1 Summary and Contributions 104 7.2 Limitations and Future Research 107 Appendices 108 Chapter A MGEval Results Between the Music of Different Genres 109 Chapter B MGEval Results of CMT and Baseline Models 116 Chapter C Samples Generated by CMT 126 Bibliography 129 국문초록 144박

Rhythm, Chord and Melody Generation for Lead Sheets using Recurrent Neural Networks

Music that is generated by recurrent neural networks often lacks a sense of direction and coherence. We therefore propose a two-stage LSTM-based model for lead sheet generation, in which the harmonic and rhythmic templates of the song are produced first, after which, in a second stage, a sequence of melody notes is generated conditioned on these templates. A subjective listening test shows that our approach outperforms the baselines and increases perceived musical coherence.Comment: 8 pages, 2 figures, 3 tables, 2 appendice

Emotion-Conditioned Melody Harmonization with Hierarchical Variational Autoencoder

Existing melody harmonization models have made great progress in improving the quality of generated harmonies, but most of them ignored the emotions beneath the music. Meanwhile, the variability of harmonies generated by previous methods is insufficient. To solve these problems, we propose a novel LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the influence of emotional conditions on melody harmonization, while improving the quality of generated harmonies and capturing the abundant variability of chord progressions. Specifically, LHVAE incorporates latent variables and emotional conditions at different levels (piece- and bar-level) to model the global and local music properties. Additionally, we introduce an attention-based melody context vector at each step to better learn the correspondence between melodies and harmonies. Experimental results of the objective evaluation show that our proposed model outperforms other LSTM-based models. Through subjective evaluation, we conclude that only altering the chords hardly changes the overall emotion of the music. The qualitative analysis demonstrates the ability of our model to generate variable harmonies.Comment: Accepted by IEEE SMC 202

Emotion-Guided Music Accompaniment Generation Based on Variational Autoencoder

Music accompaniment generation is a crucial aspect in the composition process. Deep neural networks have made significant strides in this field, but it remains a challenge for AI to effectively incorporate human emotions to create beautiful accompaniments. Existing models struggle to effectively characterize human emotions within neural network models while composing music. To address this issue, we propose the use of an easy-to-represent emotion flow model, the Valence/Arousal Curve, which allows for the compatibility of emotional information within the model through data transformation and enhances interpretability of emotional factors by utilizing a Variational Autoencoder as the model structure. Further, we used relative self-attention to maintain the structure of the music at music phrase level and to generate a richer accompaniment when combined with the rules of music theory.Comment: Accepted By International Joint Conference on Neural Networks 2023(IJCNN2023