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研究生： 蘇冠武
Kuan-Wu Su
論文名稱： 條件式具主觀成分異質內容之生成方法
Conditional Content Generation Based on Subjective Perception Context from Heterogeneous Content Types
指導教授： 呂政修
Jenq-Shiou Leu
口試委員: 呂政修
Jenq-Shiou Leu
周承復
Cheng-Fu Chou
魏宏宇
Hung-Yu Wei
陳俊良
Jiann-Liang Chen
陳郁堂
Yie-Tarng Chen
衛信文
Hsin-Wen Wei
方文賢
Wen-Hsien Fang
鄭瑞光
Ray-Guang Cheng
阮聖彰
Shanq-Jang Ruan
學位類別： 博士
Doctor
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 123
中文關鍵詞： 生成模型 、條件式生成 、深度學習 、主觀認知差異 、音樂生成 、圍棋
外文關鍵詞： Generative Models, Conditional Content Generation, Deep Learning, Subjective Perspective, Music Generation, The Game of Go
相關次數： 點閱：293  下載：4 
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    • 近幾年來生成模型發展迅速，利用人工智慧進行自動內容生成是深度學習的下一步巨大進展，並將產生許多革命的新應用。各種以內容搜尋為主的服務正逐漸轉變成為自動內容生成為主，從查詢關鍵字並爬梳關聯內容，轉為利用特製提示來直接產生所需的內容。但自動生成的內容目前往往仍需要多次試誤，產生許多不同的變化並從中精挑細選。而且有些使用者真正想要的內容，會因提示語言本身的限制，和模型缺乏某些專門領域知識導致難以生成，需額外訓練資料並花時間重新微調生成模型。有鑑於這些缺陷，本研究從使用者對不同性質的內容具有不同主觀觀感出發，利用條件式生成模型去客製化生成內容。並以不同棋手對圍棋著手的評估差異為例，生成出對應不同主觀經驗的個人客製化觀感音樂旋律。生成出來的音樂與乾淨的高品質鋼琴演奏樂間的佛雷歇距離平均只有1.687，接近錄音室錄製的品質。同時具有利用基因鑲嵌轉譯模組與其他生成模型橋接的彈性。

    Generative models had advanced significantly in recent years, and AI-powered content creation is the next big breakthrough for deep learning applications. Content-based Multimedia Information Retrieval gives way to Content-based Multimedia Generation and Creation. Instead of finding a list of correlated contents from queries, Conditional Content Generation creates brand new contents based on carefully-crafted prompts. But time consuming trial-and-Error is still required with many operating iterations to produce enough contents where only some of them are preferred outcomes. Moreover, due to the lack of specific domain knowledge, and the limitation in language expressions for prompts, some desirable outcomes might not be possible without fine-tuning models and additional training with domain-specific datasets. Hence in this dissertation, a conditional content generation method embedded different subjective perspectives across heterogeneous content types is proposed, and demonstrated by utilizing different opinions of moves in the game of Go to generate associated contextual music segments. The result of the generated music on average has 1.687 Fréchet Audio Distance (FAD) score compared to a clean virtuosic classical piano music dataset, nearly at the level of studio recording quality. It also has the flexibility to combine with other generation models through the use of genetic embedding translator.

    論文摘要 I Abstract II Acknowledgments III List of Figures VI List of Tables IX Chapter 1 Introduction 1 1.1. Background 1 1.1.1. Content Generation 1 1.1.2. Information Retrieval and Conditional Content Generation 2 1.2. Motivation 5 1.2.1. Personalized Content Generation 5 1.2.2. Personalized Content Enrichment 5 1.3. Research Target 7 1.4. Research Problems 8 1.4.1. Heterogeneous Contents 8 1.4.2. Scalability and Personalization 11 1.4.3. Bridging Interpretations and Content Creation 11 1.5. Dissertation Organization 12 Chapter 2 Subjective Perceptions in Heterogeneous Contents 13 2.1. Subjective Perceptions and AIs in the Game of Go 14 2.1.1. Go Games as Spectator Events 17 2.1.2. Representations for a Go Game Position and Commentaries 19 2.1.3. Common Go Game Terminologies 20 2.1.4. Perceptions and Expressions used in Go Games 21 2.1.5. Player Proficiency and Ranks in Go Games 22 2.2. Perceptions in Music 24 Chapter 3 Related Methods and Models 26 3.1. Generative Models 26 3.1.1. Autoencoder 26 3.1.2. Generative Adversarial Networks 28 3.1.3. Recurrent Neural Networks 30 3.1.4. Transformers 32 3.2. Deep Reinforcement Learning 34 3.3. Context and Perspective Translator 37 3.3.1. Quantized Vectors and Codebook 37 3.3.2. Tokenizer and Embedding 38 3.3.3. Genetic Optimization Embedding 39 3.4. Low-level Feature Identification 41 3.4.1. Mid-Level Features Extraction 42 3.5. Spectrogram Representation 45 Chapter 4 System Structure and Methods 47 4.1. Data Gathering and Dynamic Survey Generation 47 4.2. Self and Semi Supervised Learning 47 4.3. System Architecture 49 4.3.1. Go Game Perception Interpreter Model 50 4.3.2. Musika Music Content Generation Model 54 4.3.3. Conditional Go game board and Perception Integration Generator 56 4.3.4. Genetic Embedding Translator and Controller 57 Chapter 5 Experiments and Results 60 5.1. Experimental Setup and Hardware 60 5.2. Conditional Music Generation based on Perceptions 60 5.3. Conditional Image Commentary Generation 63 Chapter 6 Conclusion 67 6.1. Discussion 67 6.2. Conclusion 68 6.3. Future Works 69 References 71 Appendix A Go Game Basics and Terminologies 80 Appendix B Perceptions of Patterns in Go Game Positions 88 Appendix C Music Elements Aligned with Perceptions for Go Games 94 C.1. Music Associated Elements 94 C.1.1. Pitch and Note-Value 94 C.1.2. Measures and Themes 95 C.1.3. Tempo and Beats 95 C.1.4. Rhythm and Accent 96 C.1.5. Scale and Chord 96 C.2. Associated Perceptions between Music and Go Games 97 C.2.1. Height and Pitch 98 C.2.2. Big/Small and Loudness 99 C.2.3. Speed and Tempo 101 C.2.4. Strong/Soft and Rhythm 103 2.3.5. Complexity and Melody 106 Publication List 111

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