臺灣博碩士論文加值系統

傳統的麥克風陣列傅立葉近場聲全像術，透過快速傅立葉轉換(Fast Fourier Transform)能夠高效率的重建聲場，並且準確的獲得噪音源的分佈情形，但實際應用上因傅立葉轉換的特性會造成量測的誤差，且需根據聲源訊號的頻譜資訊，才能選擇主要的頻率來觀看聲場全像圖。本文利用總體經驗模態分解法(Ensemble Empirical Mode Decomposition, EEMD)的自適應性分解以及低模態混雜(Mode Mixing)特性，在滿足經驗模態分解法條件下，能夠全然從時域上對多個音源訊號分解，且利用獲得的本質模態函數(Intrinsic Mode Function, IMF)求得音源相對應的瞬時頻率，不需事先選擇主要頻譜資訊即可同步觀察各音源在聲場空間中的分佈情形。除此之外，EEMD音源成像技術亦能結合近場等效音源影像系統(Near-field Equivalent Source Imaging, NESI)中的虛擬麥克風技術來提高聲場影像的解析度。
文中利用Labview程式語言開發音源量測系統介面，實現非穩態聲場空間轉換以及EEMD近場音源成像系統，以理論模擬及實驗量測驗證球狀聲源的聲場特性，分析比較兩種全像術系統的聲場重建結果，最後藉由實驗結果深入探討給予結論。

The conventional microphone array near-field Fourier acoustic holography using Fast Fourier Transform (FFT) is able to efficiently reconstruct sound field and acquire an image of noise distribution. However, Fourier transform causes measuring error in practical applications, and people have to select primary frequency for observing sound field holography based on the spectrum of source signal. In this thesis, we use the ensemble empirical mode decomposition (EEMD) owing to its adaptive basis and low mode-mixing, which are able to decompose multiple sound sources in the time domain and acquire instantaneous frequencies by intrinsic mode functions (IMFs). Prior information about the primary frequency is not necessary by this approach that makes the simultaneous observation of each source possible. In addition, EEMD sound source imaging approach may be integrated into near-field equivalent source imaging (NESI) system, which includes a virtual microphone technology generally used for sound field image enhancement.

We have implemented and compared both non-stationary sound field spatial transform system and EEMD near-field sound source separating system in Labview language. Finally, several experimental results and detailed discussions are also provided to verify the characteristics of spherical sound source.

摘要 II
ABSTRACT III
致謝 IV
目錄 V
圖片索引 VII
表索引 IX
專業名詞縮寫對照表 X

第1章 導論 1
1.1 研究動機 1
1.2 相關文獻回顧 1
1.3 本文架構 2

第2章 麥克風陣列音源量測系統 4
2.1 近場聲全像術相關研究比較 4
2.2 近場聲全像術理論 5
2.3 近場即時音源量測系統 10

第3章 經驗模態分解法 15
3.1 經驗模態分解法基礎理論 15
3.2 希爾伯特-黃轉換 19
3.3 總體經驗模態分解法 21
3.4 經驗模態分解法特性及存在問題 23
3.4.1 經驗模態分解法基本特性 23
3.4.2 濾波器庫特性 23
3.4.3 經驗模態分解法與混波問題 25

第4章 EEMD近場音源成像系統 29
4.1 EEMD音源成像技術 29
4.2 等效音源影像技術 31
4.3 EEMD近場音源成像系統架構 33

第5章 實驗量測與結果 35
5.1 實驗設備 35
5.1.1 麥克風陣列 35
5.1.2 資料擷取模組 36
5.2 麥克風間距參數設定 38
5.3 系統軟體介面 40
5.4 單音源量測實驗 41
5.4.1 單音源量測實驗方法與步驟 41
5.4.2 單音源量測數據與結果 41
5.5 雙音源量測實驗 47
5.5.1 雙音源量測實驗方法與步驟 47
5.5.2 雙音源量測數據與結果 48
5.5.3 EEMD音源成像技術結合NESI實驗結果 56
5.6 實驗結果分析 58
5.6.1 系統分析比較 58
5.6.2 誤差分析 59

第6章 結論與未來展望 64
6.1 結論 64
6.2 未來展望 64

參考文獻 66

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