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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均(Jian-Jiun Ding) | |
| dc.contributor.author | Kuo-Cyuan Kuo | en |
| dc.contributor.author | 郭國銓 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:57:38Z | - |
| dc.date.available | 2011-01-01 | |
| dc.date.copyright | 2008-06-16 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-06-03 | |
| dc.identifier.citation | Fractional Fourier Transform
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38031 | - |
| dc.description.abstract | 傅立葉分析在信號處理中佔有很重要的角色，我們常拿它來分解訊號中各頻率的成分，然而在時頻分析上仍尚嫌不足，因為傅立葉分析只能適用穩態的訊號，無法處理時變的訊號，故而發展出許多的時頻分析的工具。固然發展時頻分可以幫助我們處理時變的訊號，而擴展傅立葉這個工具本身也是另一種方式—分數傅立葉轉換。
本篇論文中主要分成兩個部份: 第一個部份為擴充傅立葉轉換的數學理論，包括了分數傅立葉轉換和線性完整轉換的理論介紹，還有分數傅立葉轉換結合時頻分析的應用，也可以推廣至光學、雷達的應用。我們推導了測不準原理在分數傅立葉轉換和線性標準轉換的不等式，還有將連續的分數傅立葉轉換從轉換成離散的方法。一維的分數傅立葉轉換可做出傳統傅立葉做不出來的濾波器、降低取樣頻率、加密等的應用，而二維的分數傅立葉轉換可應用在影像、光學上。 第二個部份是系統性的介紹時頻分析的工具，整理各種時頻分析的優缺點，同時提出三種方法可以降低時頻分析的計算，其中包含了可適性的時頻分析。將這些時頻分析的工具實際應用在音樂和人聲上，將自動簡譜產生器的構想進一步的實現，包含了去除倍頻的訊號、根據不同的訊號特性將計算量降低，還有模擬與實驗的成果。 | zh_TW |
| dc.description.abstract | Fourier analysis takes an important role in signal processing, and we often use it to decompose frequencies for further applications. However, in the time-frequency analy-sis, the Fourier transform is not good enough. Since the Fourier transform can only deal with the stationary signals but can not deal with non-stationary or time-varying signals. Although we can develop time-frequency analysis to help us deal with time-varying signals, we also can extend the math of Fourier transform itself – fractional Fourier transform.
This thesis mainly has two parts: the first part is to extend the math of the Fourier transform. This includes the theorem of the fractional Fourier transform and the linear canonical transform. The fractional Fourier transform can combine time-frequency analysis, and extend to applications of optics and Radar systems. We also derive the uncertainty principles of the fractional Fourier transform and the linear canonical trans-form, and discrete the continuous fractional Fourier transform with ei-gen-decomposition. One dimensional fractional Fourier transform can produce filters which traditional Fourier transform cannot and also reduce the sampling rate and en-cryption…. Two dimensional fractional Fourier transform can deal with image and op-tics. The second part will introduce the time-frequency distribution systematically. We list pros and cons of each time-frequency distribution and propose three methods to re-duce the computation, including adaptive time-frequency distribution. We will apply these tools to music and acoustics signals. We are going to realize some parts of the auto-transcription and discuss the problems we face and the solutions, including exceed the harmonics and computation. Finally, there are our results and simulations. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:57:38Z (GMT). No. of bitstreams: 1 ntu-97-R95942111-1.pdf: 2723435 bytes, checksum: 6e1ee8fd79d1907b25f41eb77e36f38e (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 摘要 i
Abstract iii LIST OF FIGURES vii LIST OF TABLES xiii Chapter 1 Introduction 1 Chapter 2 Fractional Fourier Transform and Linear Canonical transform 3 2.1 Definition of Fractional Fourier Transform (FrFT) 3 2.2 The Linear Canonical Transform (LCT) 7 2.3 Two-dimensional affine generalized FrFT 8 Chapter 3 Applications of FrFT 15 3.1 Filter Design 15 3.2 Sampling 18 3.3 Fourier Optics 19 3.4 Encryption 23 3.5 SAR/ISAR applications 24 Chapter 4 The Uncertainty Principle of FrFT and LCT 29 4.1 Introduction 29 4.2 Logarithmic Uncertainty Principle 30 4.3 Heisenberg’s Uncertainty Principle 32 4.4 Conclusion 34 Chapter 5 Discrete Fractional Fourier Transform 35 5.1 Brief introduction of Discrete FrFT(DFrFT) 35 5.2 Direct form of DFrFT 35 5.3 Eigen decomposition type 36 5.4 Relationship Between Continuous and Discrete FrFT 40 Chapter 6 Time-Frequency Distribution 41 6.1 Introduction 41 6.2 Short Time Fourier Transform and Gabor Transform 41 6.3 Wigner Distribution (WDF) 45 6.4 Gabor-Wigner Distribution 47 6.5 Cohen class 49 6.6 Interference Suppression in the Wigner Distribution 55 6.7 Improvement of Time-Frequency Distribution 56 Chapter 7 Characters of Acoustic and Music Signals Analysis 61 7.1 Characteristics of Acoustic Signals 61 7.2 Sense of Hearing 63 7.3 Acoustic Signals Recognition 66 7.4 Characteristics of Music Signals 68 Chapter 8 Music Signal Analyzed by Time-Frequency Distribution 71 8.1 Why Time-Frequency Distribution 71 8.2 Monophonic and Polyphonic 71 8.3 Problems and Solutions 74 8.4 Adaptive Time-Frequency Distribution 83 Chapter 9 Acoustic Signal Analyzed by Time- Frequency Distribution 91 9.1 Signal processing of sound 91 9.2 Problems of Acoustic Signals Analysis and Discussions 100 Chapter 10 Conclusions and Future Work 103 References 105 | |
| dc.language.iso | en | |
| dc.subject | 線性完整轉換 | zh_TW |
| dc.subject | 分數傅立葉轉換 | zh_TW |
| dc.subject | 測不準原理 | zh_TW |
| dc.subject | 可適性時頻分析 | zh_TW |
| dc.subject | 時頻分析 | zh_TW |
| dc.subject | uncertainty principle | en |
| dc.subject | fractional Fourier transform | en |
| dc.subject | linear canonical transform | en |
| dc.subject | time-frequency distribution | en |
| dc.subject | adaptive time-frequency distribution | en |
| dc.title | 分數傅利葉轉換與時頻分析及其在聲音訊號上的應用 | zh_TW |
| dc.title | Fractional Fourier Transform and Time-Frequency Analysis and Apply to Acoustic Signals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 葉敏宏,王鵬華,曾益聰 | |
| dc.subject.keyword | 分數傅立葉轉換,線性完整轉換,測不準原理,時頻分析,可適性時頻分析, | zh_TW |
| dc.subject.keyword | fractional Fourier transform,linear canonical transform,uncertainty principle,time-frequency distribution,adaptive time-frequency distribution, | en |
| dc.relation.page | 115 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-06-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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