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研究生:陳如琰
研究生(外文):Chen, Ju-Yen
論文名稱:使用機器學習與聲源濾波模型合成吹管樂器與其氣音
論文名稱(外文):Synthesis of Wind Instruments and the Blowing Noise by Using Source-Filter Model with LSTM Neural Networks
指導教授:蘇文鈺蘇文鈺引用關係
指導教授(外文):Su, Wen-Yu
口試委員:胡敏君劉奕汶楊奕軒蘇黎
口試委員(外文):Hu, Min-ChunLiu, Yi-WenYang, Yi-HsuanSu, Li
口試日期:2021-06-25
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:43
中文關鍵詞:音樂合成數位波導理論機器學習長短期記憶網路聲源與濾波器建模法
外文關鍵詞:Digital WaveguidePhysical modelSource-Filter ModelMachine LearningLong Short-Term Memory Networks
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數位波導理論(Digital Waveguide Filters)已被應用於樂器聲音合成許多年,然其在設計濾波器係數以合成特定樂器之音色需要耗費相當大的計算量。在這方面,隨著神經網路的發展,一種結合機器學習的聲源與濾波器建模法(Source-filter model)在合成小提琴複雜且多樣化的演奏技巧上,已有非常著眼的表現。然而,此方法缺少對任何吹奏樂器都非常重要的雜音,以及氣音的合成。因此,在本論文中,我們以吹管樂器中的法國號及單簧管為例,根據數位波導理論為基礎來模擬管樂聲音的合成,符合吹管樂器的物理行為,並且應用長短期記憶網路模型(Long short-term memory, LSTM)預測重要參數,盡可能保留管樂應有的音色,充分結合了兩種合成方法的優勢。另外我們亦根據參數調整合成出具有不同雜音程度的聲音。在本文實驗中,我們以Real World Computing(RWC)數據庫中的預錄音檔作為訓練資料,來呈現我們方法的成效。結果顯示我們不僅能透過更動聲源(source)輕易地改變音高(pitch)與音長(duration),且能將聲音的動態變化即時呈現,亦能合成出伴隨管樂演奏的氣音,在合成處理計算效率高之下,聲音變得豐富、逼真。
Digital Waveguide Filter (DWF) has been applied to the synthesis of wind instruments for many years. To design the filter coefficients of synthesizing the timbre of a particular instrument usually takes lots of time and effort. In this respect, a novel method of combining source-filter model and a recurrent neural network using Long short-term memory (LSTM) for the synthesis of the violin has been successfully proposed. However, it lacks concerning the synthesis of the noise part which is important for playing a wind instrument. In this study, we use the DWF for simulating the noise part and apply the LSTM based source-filter model to preserve the timbre of the sound. We synthesize the sound of French horn and clarinet to demonstrate this work. The pre-recording tones from the Real-World Computing (RWC) database are used in our experiment. The result of the sound we synthesized is close to the real tone and accompanied by a pitch-synchronized noise. By altering the source, the pitch and duration of the sound we synthesized can be changed easily. Furthermore, the proposed method can be effectively implemented to different musical instruments and various playing techniques. It is proposed this synthesis method be applied to the VST plugin.
摘要 i
Abstract ii
Acknowledgements iii
Table of Contents iV
List of Figures V

Chapter 1. Introduction 1

Chapter 2. Related Work 3
2.1. Wind instrument 3
2.1.1. The French horn 3
2.1.2. The Clarinet 5
2.1.3. Common Synthesis Method 5
2.2. Physical Modeling in Wind Instrument 8
2.2.1. Digital Waveguide Filter 8
2.2.2. Physical Characteristic of Reed 11
2.3. Source-Filter Model Synthesis Using Recurrent Neural Networks 14
2.3.1. Source-Filter Model 14
2.3.2. Long Short-Term Memory Neural Network 15
2.3.3. LSTM Time-Varying Source-Filter Model 17

Chapter 3. Method 19
3.1. The Proposed Synthesis System 19
3.2. The Time-Varying Filter 22
3.3. The Synthesis of Blowing Noise 23
3.4. The Influence of the Reed Table 26

Chapter 4. Result 28
4.1. Experiments Setup 28
4.2. Synthesis Results 29
4.3. Discussion 36

Chapter 5. Conclusion 39
5.1. Conclusion 39
5.2. Future Works 40
References 41
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