심층 신경망 기반의 음악 리드 시트 자동 채보 및 멜로디 유사도 평가

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2023. 2. 이경식.Since the composition, arrangement, and distribution of music became convenient thanks to the digitization of the music industry, the number of newly supplied music recordings is increasing. Recently, due to platform environments being established whereby anyone can become a creator, user-created music such as their songs, cover songs, and remixes is being distributed through YouTube and TikTok. With such a large volume of musical recordings, the demand to transcribe music into sheet music has always existed for musicians. However, it requires musical knowledge and is time-consuming. This thesis studies automatic lead sheet transcription using deep neural networks. The development of transcription artificial intelligence (AI) can greatly reduce the time and cost for people in the music industry to find or transcribe sheet music. In addition, since the conversion from music sources to the form of digital music is possible, the applications could be expanded, such as music plagiarism detection and music composition AI. The thesis first proposes a model recognizing chords from audio signals. Chord recognition is an important task in music information retrieval since chords are highly abstract and descriptive features of music. We utilize a self-attention mechanism for chord recognition to focus on certain regions of chords. Through an attention map analysis, we visualize how attention is performed. It turns out that the model is able to divide segments of chords by utilizing the adaptive receptive field of the attention mechanism. This thesis proposes a note-level singing melody transcription model using sequence-to-sequence transformers. Overlapping decoding is introduced to solve the problem of the context between segments being broken. Applying pitch augmentation and adding a noisy dataset with data cleansing turns out to be effective in preventing overfitting and generalizing the model performance. Ablation studies demonstrate the effects of the proposed techniques in note-level singing melody transcription, both quantitatively and qualitatively. The proposed model outperforms other models in note-level singing melody transcription performance for all the metrics considered. Finally, subjective human evaluation demonstrates that the results of the proposed models are perceived as more accurate than the results of a previous study. Utilizing the above research results, we introduce the entire process of an automatic music lead sheet transcription. By combining various music information recognized from audio signals, we show that it is possible to transcribe lead sheets that express the core of popular music. Furthermore, we compare the results with lead sheets transcribed by musicians. Finally, we propose a melody similarity assessment method based on self-supervised learning by applying the automatic lead sheet transcription. We present convolutional neural networks that express the melody of lead sheet transcription results in embedding space. To apply self-supervised learning, we introduce methods of generating training data by musical data augmentation techniques. Furthermore, a loss function is presented to utilize the training data. Experimental results demonstrate that the proposed model is able to detect similar melodies of popular music from plagiarism and cover song cases.음악 산업의 디지털화를 통해 음악의 작곡, 편곡 및 유통이 편리해졌기 때문에 새롭게 공급되는 음원의 수가 증가하고 있다. 최근에는 누구나 크리에이터가 될 수 있는 플랫폼 환경이 구축되어, 사용자가 만든 자작곡, 커버곡, 리믹스 등이 유튜브, 틱톡을 통해 유통되고 있다. 이렇게 많은 양의 음악에 대해, 음악을 악보로 채보하고자 하는 수요는 음악가들에게 항상 존재했다. 그러나 악보 채보에는 음악적 지식이 필요하고, 시간과 비용이 많이 소요된다는 문제점이 있다. 본 논문에서는 심층 신경망을 활용하여 음악 리드 시트 악보 자동 채보 기법을 연구한다. 채보 인공지능의 개발은 음악 종사자 및 연주자들이 악보를 구하거나 만들기 위해 소모하는 시간과 비용을 크게 줄여 줄 수 있다. 또한 음원에서 디지털 악보 형태로 변환이 가능해지므로, 자동 표절 탐지, 작곡 인공지능 학습 등 다양하게 활용이 가능하다. 리드 시트 채보를 위해, 먼저 오디오 신호로부터 코드를 인식하는 모델을 제안한다. 음악에서 코드는 함축적이고 표현적인 음악의 중요한 특징이므로 이를 인식하는 것은 매우 중요하다. 코드 구간 인식을 위해, 어텐션 매커니즘을 이용하는 트랜스포머 기반 모델을 제시한다. 어텐션 지도 분석을 통해, 어텐션이 실제로 어떻게 적용되는지 시각화하고, 모델이 코드의 구간을 나누고 인식하는 과정을 살펴본다. 그리고 시퀀스 투 시퀀스 트랜스포머를 이용한 음표 수준의 가창 멜로디 채보 모델을 제안한다. 디코딩 과정에서 각 구간 사이의 문맥 정보가 단절되는 문제를 해결하기 위해 중첩 디코딩을 도입한다. 데이터 변형 기법으로 음높이 변형을 적용하는 방법과 데이터 클렌징을 통해 학습 데이터를 추가하는 방법을 소개한다. 정량 및 정성적인 비교를 통해 제안한 기법들이 성능 개선에 도움이 되는 것을 확인하였고, 제안모델이 MIR-ST500 데이터 셋에 대한 음표 수준의 가창 멜로디 채보 성능에서 가장 우수한 성능을 보였다. 추가로 주관적인 사람의 평가에서 제안 모델의 채보 결과가 이전 모델보다 저 정확하다고 인식됨을 확인하였다. 앞의 연구의 결과를 활용하여, 음악 리드 시트 자동 채보의 전체 과정을 제시한다. 오디오 신호로부터 인식한 다양한 음악 정보를 종합하여, 대중 음악 오디오 신호의 핵심을 표현하는 리드 시트 악보 채보가 가능함을 보인다. 그리고 이를 전문가가 제작한 리드시트와 비교하여 분석한다. 마지막으로 리드 시트 악보 자동 채보 기법을 응용하여, 자기 지도 학습 기반 멜로디 유사도 평가 방법을 제안한다. 리드 시트 채보 결과의 멜로디를 임베딩 공간에 표현하는 합성곱 신경망 모델을 제시한다. 자기지도 학습 방법론을 적용하기 위해, 음악적 데이터 변형 기법을 적용하여 학습 데이터를 생성하는 방법을 제안한다. 그리고 준비된 학습 데이터를 활용하는 심층 거리 학습 손실함수를 설계한다. 실험 결과 분석을 통해, 제안 모델이 표절 및 커버송 케이스에서 대중음악의 유사한 멜로디를 탐지할 수 있음을 확인한다.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 Attention Mechanism and Transformers 7 2.1.1 Attention-based Models 7 2.1.2 Transformers with Musical Event Sequence 8 2.2 Chord Recognition 11 2.3 Note-level Singing Melody Transcription 13 2.4 Musical Key Estimation 15 2.5 Beat Tracking 17 2.6 Music Plagiarism Detection and Cover Song Identi cation 19 2.7 Deep Metric Learning and Triplet Loss 21 Chapter 3 Problem De nition 23 3.1 Lead Sheet Transcription 23 3.1.1 Chord Recognition 24 3.1.2 Singing Melody Transcription 25 3.1.3 Post-processing for Lead Sheet Representation 26 3.2 Melody Similarity Assessment 28 Chapter 4 A Bi-directional Transformer for Musical Chord Recognition 29 4.1 Methodology 29 4.1.1 Model Architecture 29 4.1.2 Self-attention in Chord Recognition 33 4.2 Experiments 35 4.2.1 Datasets 35 4.2.2 Preprocessing 35 4.2.3 Evaluation Metrics 36 4.2.4 Training 37 4.3 Results 38 4.3.1 Quantitative Evaluation 38 4.3.2 Attention Map Analysis 41 Chapter 5 Note-level Singing Melody Transcription 44 5.1 Methodology 44 5.1.1 Monophonic Note Event Sequence 44 5.1.2 Audio Features 45 5.1.3 Model Architecture 46 5.1.4 Autoregressive Decoding and Monophonic Masking 47 5.1.5 Overlapping Decoding 47 5.1.6 Pitch Augmentation 49 5.1.7 Adding Noisy Dataset with Data Cleansing 50 5.2 Experiments 51 5.2.1 Dataset 51 5.2.2 Experiment Con gurations 52 5.2.3 Evaluation Metrics 53 5.2.4 Comparison Models 54 5.2.5 Human Evaluation 55 5.3 Results 56 5.3.1 Ablation Study 56 5.3.2 Note-level Transcription Model Comparison 59 5.3.3 Transcription Performance Distribution Analysis 59 5.3.4 Fundamental Frequency (F0) Metric Evaluation 60 5.4 Qualitative Analysis 62 5.4.1 Visualization of Ablation Study 62 5.4.2 Spectrogram Analysis 65 5.4.3 Human Evaluation 67 Chapter 6 Automatic Music Lead Sheet Transcription 68 6.1 Post-processing for Lead Sheet Representation 68 6.2 Lead Sheet Transcription Results 71 Chapter 7 Melody Similarity Assessment with Self-supervised Convolutional Neural Networks 77 7.1 Methodology 77 7.1.1 Input Data Representation 77 7.1.2 Data Augmentation 78 7.1.3 Model Architecture 82 7.1.4 Loss Function 84 7.1.5 De nition of Distance between Songs 85 7.2 Experiments 87 7.2.1 Dataset 87 7.2.2 Training 88 7.2.3 Evaluation Metrics 88 7.3 Results 89 7.3.1 Quantitative Evaluation 89 7.3.2 Qualitative Evaluation 99 Chapter 8 Conclusion 107 8.1 Summary and Contributions 107 8.2 Limitations and Future Research 110 Bibliography 111 국문초록 126박

Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model

Numerous studies in the field of music generation have demonstrated impressive performance, yet virtually no models are able to directly generate music to match accompanying videos. In this work, we develop a generative music AI framework, Video2Music, that can match a provided video. We first curated a unique collection of music videos. Then, we analysed the music videos to obtain semantic, scene offset, motion, and emotion features. These distinct features are then employed as guiding input to our music generation model. We transcribe the audio files into MIDI and chords, and extract features such as note density and loudness. This results in a rich multimodal dataset, called MuVi-Sync, on which we train a novel Affective Multimodal Transformer (AMT) model to generate music given a video. This model includes a novel mechanism to enforce affective similarity between video and music. Finally, post-processing is performed based on a biGRU-based regression model to estimate note density and loudness based on the video features. This ensures a dynamic rendering of the generated chords with varying rhythm and volume. In a thorough experiment, we show that our proposed framework can generate music that matches the video content in terms of emotion. The musical quality, along with the quality of music-video matching is confirmed in a user study. The proposed AMT model, along with the new MuVi-Sync dataset, presents a promising step for the new task of music generation for videos

Generating Chord Progression from Melody with Flexible Harmonic Rhythm and Controllable Harmonic Density

Melody harmonization, which involves generating a chord progression that complements a user-provided melody, continues to pose a significant challenge. A chord progression must not only be in harmony with the melody, but also interdependent on its rhythmic pattern. While previous neural network-based systems have been successful in producing chord progressions for given melodies, they have not adequately addressed controllable melody harmonization, nor have they focused on generating harmonic rhythms with flexibility in the rates or patterns of chord changes. This paper presents AutoHarmonizer, a novel system for harmonic density-controllable melody harmonization with such a flexible harmonic rhythm. AutoHarmonizer is equipped with an extensive vocabulary of 1,462 chord types and can generate chord progressions that vary in harmonic density for a given melody. Experimental results indicate that the AutoHarmonizer-generated chord progressions exhibit a diverse range of harmonic rhythms and that the system's controllable harmonic density is effective.Comment: 12 pages, 6 figures, 1 table, accepted by EURASIP JASM

Transformers in Machine Learning: Literature Review

In this study, the researcher presents an approach regarding methods in Transformer Machine Learning. Initially, transformers are neural network architectures that are considered as inputs. Transformers are widely used in various studies with various objects. The transformer is one of the deep learning architectures that can be modified. Transformers are also mechanisms that study contextual relationships between words. Transformers are used for text compression in readings. Transformers are used to recognize chemical images with an accuracy rate of 96%. Transformers are used to detect a person's emotions. Transformer to detect emotions in social media conversations, for example, on Facebook with happy, sad, and angry categories. Figure 1 illustrates the encoder and decoder process through the input process and produces output. the purpose of this study is to only review literature from various journals that discuss transformers. This explanation is also done by presenting the subject or dataset, data analysis method, year, and accuracy achieved. By using the methods presented, researchers can conclude results in search of the highest accuracy and opportunities for further research

A Review of Intelligent Music Generation Systems

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects

MARBLE: Music Audio Representation Benchmark for Universal Evaluation

In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark. To address this issue, we introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines. Besides, MARBLE offers an easy-to-use, extendable, and reproducible suite for the community, with a clear statement on copyright issues on datasets. Results suggest recently proposed large-scale pre-trained musical language models perform the best in most tasks, with room for further improvement. The leaderboard and toolkit repository are published at this https URL to promote future music AI research

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