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

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

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

Toward Interactive Music Generation: A Position Paper

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations

NBP 2.0: Updated Next Bar Predictor, an Improved Algorithmic Music Generator

Deep neural network advancements have enabled machines to produce melodies emulating human-composed music. However, the implementation of such machines is costly in terms of resources. In this paper, we present NBP 2.0, a refinement of the previous model next bar predictor (NBP) with two notable improvements: first, transforming each training instance to anchor all the notes to its musical scale, and second, changing the model architecture itself. NBP 2.0 maintained its straightforward and lightweight implementation, which is an advantage over the baseline models. Improvements were assessed using quantitative and qualitative metrics and, based on the results, the improvements from these changes made are notable