請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張智星 | zh_TW |
| dc.contributor.advisor | Jyh-Shing Roger Jang | en |
| dc.contributor.author | 陳宥華 | zh_TW |
| dc.contributor.author | Yu-Hua Chen | en |
| dc.date.accessioned | 2025-08-21T17:08:32Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-02 | - |
| dc.identifier.citation | [1] DI unit. [Online] https://en.wikipedia.org/wiki/DI_unit.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99289 | - |
| dc.description.abstract | 電吉他是現代音樂的重要元素，其與原聲吉他及鋼琴的最大區別，在於其音色高度依賴經由擴大器與效果器處理後所產生的各種變化。然而，相較於以鋼琴為主的研究，吉他導向的音樂資訊檢索（Music Information Retrieval, MIR）發展相對落後，主因包括資料集稀缺（受限於版權與蒐集困難）以及效果處理音訊所需的特殊表示需求。本論文致力於推進吉他導向的 MIR，提出兩項核心貢獻：首先，建立並釋出兩個新資料集──Electric Guitar Database（EGDB）及其擴充版本 EGDB-PG；其次，針對兩項關鍵表示轉換任務設計創新深度學習演算法：（1）透過電吉他自動轉譜，實現由效果處理音訊轉換為符號樂譜的 audio-to-score 轉換；（2）透過電吉他音箱音色建模，實現由乾淨音色轉換為帶有效果的音色的 clean-to-wet 轉換，並探索無監督與零樣本等深度學習場景以重建多樣音色效果。透過實驗驗證，本研究有效提升吉他音訊、樂譜與音色的表示能力，有效解決電吉他效果處理所帶來的挑戰，為更穩健的基於深度學習的吉他音樂分析與吉他音箱模擬奠定基礎。 | zh_TW |
| dc.description.abstract | Electric guitars, central to modern music, are distinguished from acoustic guitars and pianos by their reliance on effects processing through amplifiers and pedals, which introduces complex tonal variations. However, guitar-oriented music information retrieval (MIR) lags behind piano-oriented research due to scarce datasets, constrained by copyright and collection challenges, and the unique representational demands of effect-laden audio. This thesis advances guitar-oriented MIR by curating novel datasets, the Electric Guitar Database (EGDB) and its expanded version (EGDB-PG), and proposing original deep learning algorithms for two key representation transformations: (1) audio-to-score transformation via electric guitar transcription, leveraging EGDB and EGDB-PG to map effect-processed audio to symbolic notation; and (2) clean-to-wet audio transformation through electric guitar amplifier tone modeling, exploring unsupervised and zero-shot paradigms to replicate diverse effects. Validated through empirical evaluations, these contributions enhance audio, score, and tone representations in guitar-oriented MIR, addressing the distinct challenges posed by electric guitar effects and paving the way for robust deep-learning based electric guitar music analysis and effect modeling systems. | en |
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| dc.description.tableofcontents | Acknowledgements i
摘要 iii Abstract v Contents vii List of Figures xiii List of Tables xvii Chapter 1 Introduction 1 1.1 Guitar in Music Information Retrieval . . . . . . . . . . . . . . . . . 1 1.2 Representation and Transformation in Guitar Music . . . . . . . . . . 2 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Background 7 2.1 Acoustic Guitar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Electric Guitar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Guitar Amplifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.1 How Amplifiers Shape Tone . . . . . . . . . . . . . . . . . . . . . 9 2.3.2 Effects and Amplifiers in MIR . . . . . . . . . . . . . . . . . . . . 10 Chapter 3 Dataset 13 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Related Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.1 IDMT-SMT-GUITAR Database . . . . . . . . . . . . . . . . . . . . 14 3.2.2 GuitarSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.3 Guitar Playing Techniques Database . . . . . . . . . . . . . . . . . 15 3.2.4 SynthTab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 EGDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3.2 Audio Collection Process . . . . . . . . . . . . . . . . . . . . . . . 17 3.3.3 Annotation Process . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.4 Timbre Re-render Process . . . . . . . . . . . . . . . . . . . . . . . 19 Chapter 4 Electric Guitar Transcription 21 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2.1 Proposed Transcription Model . . . . . . . . . . . . . . . . . . . . 24 4.2.1.1 Transformer Encoder . . . . . . . . . . . . . . . . . . 26 4.2.1.2 Transformer Decoder . . . . . . . . . . . . . . . . . . 26 4.2.1.3 Implementation Details . . . . . . . . . . . . . . . . . 27 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3.1 Evaluation on DI Recordings . . . . . . . . . . . . . . . . . . . . . 28 4.3.2 Evaluation on Unseen Timbres Rendered with Amps . . . . . . . . 29 4.3.3 Evaluation on Real-world Recordings . . . . . . . . . . . . . . . . 30 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Chapter 5 Unsupervised Amplifier Modeling 33 5.1 Tone Modeling in Commercial Products . . . . . . . . . . . . . . . . 33 5.2 Neural Virtual Analog Modeling . . . . . . . . . . . . . . . . . . . . 35 5.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.3.1 Neural Amplifier Modeling . . . . . . . . . . . . . . . . . . . . . . 37 5.3.2 Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . 39 5.3.3 Backbone Model for Generator . . . . . . . . . . . . . . . . . . . . 39 5.3.4 Discriminators for GANs Training . . . . . . . . . . . . . . . . . . 40 5.3.5 Clean Audio from Existing Datasets . . . . . . . . . . . . . . . . . 41 5.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.4.1 Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.4.2 Discriminator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.4.3 GAN Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.5 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.5.2 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.5.3 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . 48 5.5.4 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.6 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.6.1 Comparison with Baseline Methods . . . . . . . . . . . . . . . . . 50 5.6.2 Clean Audio Combination . . . . . . . . . . . . . . . . . . . . . . 51 5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.7.1 Benefit of the GAN-based Approach for VA Modeling . . . . . . . 54 5.7.2 Artifacts Generated by the Proposed Model . . . . . . . . . . . . . 55 5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Chapter 6 One-to-Many and Zero-Shot Amplifier Modeling 57 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.3 Multi-Tone Amplifier Modeling . . . . . . . . . . . . . . . . . . . . 63 6.3.1 Tone Embedding Encoder . . . . . . . . . . . . . . . . . . . . . . . 63 6.3.2 Conditional Generator . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.3.3 Source of the Reference Audio Signal . . . . . . . . . . . . . . . . 67 6.3.4 Zero-Shot Tone Transfer . . . . . . . . . . . . . . . . . . . . . . . 68 6.3.5 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . 69 6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.1 Tone Embedding Visualization . . . . . . . . . . . . . . . . . . . . 70 6.4.2 Efficacy of One-to-Many Neural Amp Modeling . . . . . . . . . . 71 6.4.3 Zero-Shot Learning on Unseen Amplifiers . . . . . . . . . . . . . . 73 6.4.4 Case Study on Zero-Shot Amp Tone Transfer . . . . . . . . . . . . 74 6.5 Plugin Version in DAW . . . . . . . . . . . . . . . . . . . . . . . . 75 6.5.1 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . 77 6.5.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6.5.3 Real-Time Plugin . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Chapter 7 Electric Guitar Transcription with Large-Scale DatasetS 81 7.1 Tone and Presets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.3 Challenges for Amplifier-Rendered Automatic Guitar Transcription . 83 7.3.1 Limited Data Quantity in Datasets . . . . . . . . . . . . . . . . . . 84 7.3.2 Lack of Tone Diversity . . . . . . . . . . . . . . . . . . . . . . . . 84 7.3.3 Complexities of Tablature Format from Guitar Fretboard Design . . 85 7.3.4 Expressive Playing Techniques . . . . . . . . . . . . . . . . . . . . 85 7.3.5 Current Efforts and Remaining Gaps . . . . . . . . . . . . . . . . . 86 7.4 Tone-Informed MIR . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7.5 EGDB-PG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7.6 Amplifier-Rendered Transcription Approaches . . . . . . . . . . . . 89 7.6.1 Tone Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.7 Transcription System . . . . . . . . . . . . . . . . . . . . . . . . . . 91 7.7.1 hFT-Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 7.7.2 Architecture of Tone-informed Transformer . . . . . . . . . . . . . 92 7.7.3 Content Augmentation . . . . . . . . . . . . . . . . . . . . . . . . 94 7.7.4 Audio Normalization . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.8 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.8.1 Impact of Content Augmentation and Audio Normalization on Transcription Performance . . . . . . . . . . . . . . . . . . . . . . . . . 96 7.8.2 Baseline Performance with Audio Normalization . . . . . . . . . . 97 7.8.3 Effect of Content Augmentation . . . . . . . . . . . . . . . . . . . 98 7.8.4 Impact of Audio Normalization under Content Augmentation . . . . 98 7.8.5 Performance without Tone Embeddings . . . . . . . . . . . . . . . 99 7.8.6 Comparison with Other Transcription Models . . . . . . . . . . . . 99 7.8.7 Evaluating Generalizability to Tone and Content Variations . . . . . 102 7.8.8 Impact of Tone Augmentation (# of Tones) . . . . . . . . . . . . . . 103 7.8.9 Impact of Content Augmentation . . . . . . . . . . . . . . . . . . . 104 7.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Chapter 8 Conclusions and Future Work 107 8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 References 109 | - |
| dc.language.iso | en | - |
| dc.subject | 電吉他 | zh_TW |
| dc.subject | 虛擬類比建模 | zh_TW |
| dc.subject | 音訊效果建模 | zh_TW |
| dc.subject | 音樂資訊檢索 | zh_TW |
| dc.subject | 轉譜 | zh_TW |
| dc.subject | Transcription | en |
| dc.subject | Music Information Retrieval | en |
| dc.subject | Electric Guitar | en |
| dc.subject | Effect Modeling | en |
| dc.subject | Virtual Analog Modeling | en |
| dc.title | 從音訊到樂譜與音色：探討以吉他為核心的音樂資訊 檢索中的表示法與轉換 | zh_TW |
| dc.title | From Audio to Score and Tone: Exploring Representations and Transformations in Guitar-Oriented Music Information Retrieval | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 楊奕軒 | zh_TW |
| dc.contributor.coadvisor | Yi-Hsuan Yang | en |
| dc.contributor.oralexamcommittee | 李宏毅;蘇黎;王新民 | zh_TW |
| dc.contributor.oralexamcommittee | Hung-Yi Lee;Li Su;Hsin-Min Wang | en |
| dc.subject.keyword | 音樂資訊檢索,電吉他,轉譜,虛擬類比建模,音訊效果建模, | zh_TW |
| dc.subject.keyword | Music Information Retrieval,Electric Guitar,Transcription,Virtual Analog Modeling,Effect Modeling, | en |
| dc.relation.page | 123 | - |
| dc.identifier.doi | 10.6342/NTU202502953 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-06 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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