基於深度學習之即時電吉他效果器模擬與虛擬實境空氣吉他系統

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林季劼(Ji-Jie Lin) 查詢紙本館藏

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論文名稱

基於深度學習之即時電吉他效果器模擬與虛擬實境空氣吉他系統
(A Deep Learning-based Approach for a Black-box Real-time Guitar Amplifier Emulation and a VR-based Air Guitar System)

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摘要(中)

近年來，虛擬實境的發展已成為大眾關注的焦點。隨著越來越多關於虛擬實境的產品和應用的出現，
虛擬實境設備的效能不斷提升，成本也大幅下降，逐漸成為人手必備的設備。
虛擬實境帶來的沉浸體驗不僅為使用者提供視覺上的享受，還提供了與傳統方式不同的互動方式。
透過虛擬實境技術，使用者可以在虛擬環境中進行各種活動，如遊戲、會議、教育和醫療等。
虛擬實境技術的重要性不斷增加，其中在虛擬樂器領域的應用也越來越廣泛，例如虛擬鋼琴和虛擬爵士鼓等。
這些虛擬樂器的出現不僅讓使用者在虛擬環境中體驗彈奏樂器的樂趣，還降低了使用不同樂器的門檻，
不受地點、時間、空間、設備和技術的限制，只要擁有虛擬實境設備，就能隨時隨地享受彈奏樂器的樂趣。
因此，虛擬音樂會也受到越來越多的重視，如虛擬空間音場模擬和歷史演場會的3D重建等。

然而，在過去的研究中，虛擬吉他多數在非虛擬實境的環境中進行，主要集中在對空氣吉他和弦的識別上。
目前尚未有系統性地研究虛擬實境中的虛擬空氣吉他系統。而在商業化的虛擬吉他遊戲中，
對手部姿勢的識別並不精準，僅能辨識手部是否彎曲和是否刷弦，無法準確辨識和弦和多樣的刷弦動作。
因此，在本研究中，我們提出了一個虛擬空氣吉他系統，使用者只需透過虛擬實境設備即可彈奏吉他。
我們利用深度學習模型的辨識能力和虛擬實境的視覺回饋優勢，能夠辨識高達30種和弦，
並透過搖桿設備實現多種刷弦技巧。

此外，本研究還運用了Black-box方式，透過結合WaveNet和FiLM，
能夠模擬電吉他效果器在不同旋鈕值下的效果模擬，並且透過所提出的Knob Difference Loss，
進一步提高模擬效果的準確率。
在網路架構上，也提出的Kernel Dilation技巧，
在不降低準確度的情況下，將先前研究中使用的WaveNet前饋速度提高了兩倍。
使得在虛擬實境環境中的高效能運算情況下，
能夠使用Intel7 11700 K處理器（於2021年發行）和NVDIA RTX 1060（於2016年發行），
實現即時電吉他效果模擬。

摘要(英)

In recent years, the development of virtual reality (VR) has become a focal point of public
attention. With the emergence of more VR products and applications, the performance of VR
devices has continuously improved, and the cost has significantly decreased, gradually
becoming essential devices for individuals. VR provides an immersive experience that
not only offers visual enjoyment to users but also provides interactive modes different
from traditional methods. Through VR technology, users can engage in various activities
in virtual environments, such as gaming, meetings, education, and healthcare.
The significance of VR technology continues to increase, and its applications
in the virtual instrument field, such as virtual pianos and virtual jazz drums,
are becoming increasingly widespread. The emergence of these virtual instruments
not only allows users to experience the pleasure of playing instruments
in virtual environments but also lowers the barrier to learning different instruments.
With VR devices, users can enjoy playing instruments anytime and anywhere,
free from limitations of location, time, space, equipment, and technical expertise.
Consequently, virtual concerts, including virtual spatial audio simulations and 3D
reconstructions of historical performances, have gained more attention.

However, in previous studies, virtual guitars were mostly conducted in non-VR environments,
focusing primarily on the recognition of air guitar chords.
There has been a lack of systematic research on virtual air guitar systems within VR.
Moreover, commercial virtual guitar games currently have limited accuracy
in recognizing hand gestures, only able to detect finger bending and simple strumming actions,
but unable to accurately identify chords and various strumming techniques.
Therefore, in this study, we propose a virtual air guitar system that allows users
to play the guitar simply through VR devices. By leveraging the recognition capabilities
of deep learning models and the visual feedback advantages of VR, our system can recognize
up to 30 different chords and implement various strumming techniques using a joystick device.

Furthermore, we apply a black-box approach by combining WaveNet and FiLM to simulate
the effects of an electric guitar pedal at different knob settings. Additionally,
we introduce a Knob Difference Loss to improve the accuracy of the simulated effects.
In terms of the network architecture, we propose the Kernel Dilation technique,
which doubles the forward speed of WaveNet used in previous studies without sacrificing
accuracy. This enables real-time simulation of electric guitar effects even under
high-performance computing VR environments,
using an Intel 7 11700 K processor (released in 2021)
and an NVIDIA RTX 1060 graphics card (released in 2016).

關鍵字(中)

★ 深度學習
★ 虛擬實境
★ 虛擬樂器
★ 效果器模擬
★ 電腦視覺
★ 音訊處理

關鍵字(英)

★ Deep Learning
★ Virtual Reality
★ Virtual Instrument
★ Guitar Emplifier Emulation
★ Computer Vision
★ Audio Processing

論文目次

摘要 iv
Abstract vi
誌謝 viii
目錄 ix
一、緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 論文架構 4
二、背景知識以及文獻回顧 5
2.1 背景知識 5
2.1.1 吉他彈奏 5
2.1.2 真空管放大器與電吉他效果器 6
2.1.3 MIDI 事件與 MIDI 播放器 8
2.1.4 3D 手部偵測於虛擬實境 9
2.2 文獻回顧 11
2.2.1 虛擬樂器及空氣吉他相關研究 11
2.2.2 電吉他效果器模擬相關研究 13
三、研究方法 15
3.1 虛擬電吉他系統 15
3.1.1 系統架構 15
3.1.2 3D 手勢模型碰撞器及吉他指板、刷弦碰撞器 17
3.1.3 3D 和弦手勢模型 18
3.1.4 3D 手勢模型平移投影 19
3.1.5 吉他 MIDI 聲音合成 23
3.2 手勢資料前處理與和弦辨識 24
3.2.1 3D 抬頭旋轉 24
3.2.2 平移原點、旋轉固定 26
3.2.3 關節長度平均化及雜訊 27
3.2.4 手部關節角度轉換 28
3.2.5 和弦辨識與 MLP 模型 29
3.3 音訊資料前處理與資料增加 30
3.3.1 Gaussian Noise 30
3.3.2 BatchNorm1d 30
3.3.3 Extra Sample Window 31
3.4 電吉他效果器模擬損失函數 32
3.4.1 Pre-emphasis Error to Signal Ratio 32
3.4.2 Multi-resolution Short Time Fourier Transform 33
3.4.3 Knob Difference Loss 34
3.5 電吉他效果器模型 36
3.5.1 WaveNet 及 Feed Forward WaveNet 36
3.5.2 模型架構 38
3.5.3 FiLM 40
3.5.4 Dilated Causal Convolution 與 Kernel Dilation 41
3.5.5 History Buffer 43
四、實驗設計與結果 44
4.1 手勢與效果器資料集 44
4.1.1 手勢資料集和不同測試集 44
4.1.2 效果器資料集 47
4.2 3D 空間和弦辨識 49
4.2.1 和弦辨識 49
4.2.2 不同受試者之和弦辨識 50
4.2.3 3D 抬頭旋轉對和弦辨識穩定之影響 51
4.3 電吉他效果器模擬 52
4.3.1 效果器模擬 52
4.3.2 Knob Difference Loss 損失函數對預測影響 54
4.3.3 History Buffer 對前饋效果之影響 55
五、總結 56
5.1 結論 56
5.2 未來展望 57
參考文獻 58

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指導教授

蘇木春(Mu-Chun Su)

審核日期

2023-7-26

推文