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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張智星(Jyh-Shing Roger Jang) | |
| dc.contributor.author | Yu-Siang Huang | en |
| dc.contributor.author | 黃郁翔 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:07:59Z | - |
| dc.date.available | 2025-07-14 | |
| dc.date.copyright | 2020-07-17 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58195 | - |
| dc.description.abstract | 音樂生成與影像生成及影片生成有著一些顯著的差異。首先，音樂是時間上的藝術，所以我們需要利用時序處理的方法。接著，音符不僅僅是純粹時序上的先後關係，鄰近的音群可以組成各式的音樂語法、結構，例如和弦、琶音與音階等等。本篇論文，在基於自注意力機制模型的框架下，我們探討如何生成數分鐘長的流行鋼琴音樂，我們也進一步地提出一套資料前處理的流程，藉由此流程我們可以從原始音訊轉換為音樂數位介面格式。為了分析生成結果，透過主觀的使用者問卷，我們得到許多深刻的見解，並且對模型的架構優缺點做一個通盤性的探討，進一步驗證了我們提出方法的有效性，從而了解深度學習技術的有效性與局限性。 | zh_TW |
| dc.description.abstract | Generating music has a few notable differences from generating images and videos. First, music is an art of the time, necessitating a temporal model. Second, musical notes are often grouped into chords, arpeggios, or melodies in polyphonic music, and therefore introducing a sequential ordering of notes into the generating model is critical. In this thesis, we investigated the framework of the Transformer model for generating minute-long pop piano music. We also proposed a data pre-processing pipeline to collect audio data and convert it to MIDI format. To evaluate the generated results, we adopted subjective user study to demonstrate the effectiveness of the proposed method. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:07:59Z (GMT). No. of bitstreams: 1 U0001-1407202014411900.pdf: 3625414 bytes, checksum: 7150038659da4efd4b3848a351ac4f51 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書.................................. i 摘要.......................................... ii Abstract........................................ iii Contents........................................ iv List of Figures..................................... vi List of Tables ..................................... ix 1 Introduction.................................... 1 1.1 Motivation.................................. 1 1.2 Problem Statement ............................. 2 1.2.1 Design of Representation...................... 2 1.2.2 Design of Networks ........................ 3 1.3 Contribution................................. 4 1.4 Thesis Organization............................. 4 2 Related Work ................................... 5 2.1 Symbolic Music Generation ........................ 5 2.1.1 Image-modeling Approach..................... 5 2.1.2 Language-modeling Approach................... 6 2.2 Transformer................................. 7 3 Method ...................................... 9 3.1 Data Pre-processing............................9 3.1.1 Data Collection........................... 9 3.1.2 Music Transcription ........................ 10 3.1.3 Time Quantization ......................... 11 3.1.4 Data Augmentation......................... 11 3.1.5 Symbolic ChordRecognition.................... 12 3.1.6 Event Representation........................ 16 3.2 Model.................................... 18 3.2.1 Adaptive Input Representation................... 18 3.2.2 Self-Attention Modules....................... 20 3.2.3 Relative Positional Encoding.................... 23 3.2.4 Other Modules ........................... 27 3.2.5 Training with Gradient Checkpointing ............................ 30 3.2.6 Model settings ........................... 32 4 Experiments.................................... 34 4.1 Experiment Settings............................. 34 4.2 Effectiveness of Input Length........................ 36 4.3 Effectiveness of Chord Information .................... 38 4.4 General Subjective Ratings......................... 40 4.5 Generation from scratch .......................... 42 5 Conclusion and Future Work ........................... 44 Bibliography ..................................... 45 | |
| dc.language.iso | en | |
| dc.subject | 自注意力機制 | zh_TW |
| dc.subject | 流行樂 | zh_TW |
| dc.subject | 鋼琴 | zh_TW |
| dc.subject | 音樂生成 | zh_TW |
| dc.subject | music generation | en |
| dc.subject | pop | en |
| dc.subject | piano | en |
| dc.subject | Transformer | en |
| dc.title | 使用和弦編碼轉換的流行音樂鋼琴樂曲自動生成 | zh_TW |
| dc.title | Pop Piano Music Generation Using Chord-encoded Transformer | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 楊奕軒(Yi-Hsuan Yang) | |
| dc.contributor.oralexamcommittee | 蔡銘峰(Ming-Feng Tsai) | |
| dc.subject.keyword | 音樂生成,流行樂,鋼琴,自注意力機制, | zh_TW |
| dc.subject.keyword | music generation,pop,piano,Transformer, | en |
| dc.relation.page | 50 | |
| dc.identifier.doi | 10.6342/NTU202001511 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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