一般應用於迴音消除上使用的Adaptive signal processing都需要一個期望訊號，根據期望訊號與來源訊號間不斷的誤差調整，來獲得所需訊號。不過在通訊環境中，為了消除環境週遭的噪音與迴音，如何取得有用的期望訊號，根據期望訊號來適時調整取得所需訊號，將增加系統的複雜程度。
    本論文提出藉由噪音降低機制所產生的訊號透過特殊處理，使其具備迴音路徑，並根據此訊號作為迴音消除器的期望訊號，結合來源訊號做迴音消除動作，藉此簡化迴音消除器和噪音降低機制的系統架構，達到同時降低噪音與消除迴音的功能。
    本論文將實地應用單一麥克風音源錄製在汽車行駛環境內的通訊樣本，而非模擬車內環境噪音所後製的樣本，並針對錄製的樣本，使用本論文改良的系統架構進行降低噪音和消除迴音動作，以驗證在本論文系統架構中，可以有效消除在車內環境通訊時，所產生的噪音和迴音，同時達到簡化系統架構的目的。

In general, adaptive signal processing applied in echo cancellations all need a desired signal value, This desired signal is used along with the source signal as references to continuously calculate and tune up the needed signal. However, under a communications environment the effort of obtaining the signal by computing the result of the desired and source signal in order to eliminate ambient noise and echo would increase the system's complexity.
  This thesis proposes a method to reduce noise and eliminate echo without introducing excessive complications to the infrastructure of the system. Our method involves using a particular process to find echo route relations with the signals produced by the noise reduction system, and using this data as the desired signal value combined with the source signal as inputs of the echo cancellation step.
  The experiment's environmental setup involves using a single microphone as the sound input recording the sounds in a driving automobile as samples instead of capturing data from a simulated environment. We apply our improved method to the samples and perform the steps of reducing noise and eliminating echo. The result will show our method is capable of eliminating noise and echo produced in a car's interior and at the same time avoiding complexities added to our system's infrastructure.

第1章 緒論 ...................................1
1-1 研究動機與目的 ................................1
1-2 研究內容 ......................................4
1-3 論文組織 ......................................5
第2章 相關背景知識之介紹 .....................6
	2-1 Prototype adaptive filtering scheme ...........6
	2-2 Wiener filter 簡介 ............................8
	2-3 Steepest descent algorithm 簡介 ..............12
	2-4 LMS algorithm 簡介 ...........................14
	2-5 離散傅立葉轉換 ...............................18
	2-6 快速傅立葉轉換 ...............................20
	2-7 Overlap-save method VS Overlap-add method ....24
	2-8 FLMS algorithm 簡介 ......................... 28
第3章 噪音與迴音抑制系統介紹 ................32
	3-1 Noise reduction using spectral subtraction ...32
3-2 Acoustic echo cancellation using FLMS algorithm .....35
3-3 聯合噪音抑制和聲學迴音消除系統 ...............38
第4章 改良式 NR + AEC 系統架構模擬與實驗結果.41
	4-1 測試環境與實驗背景介紹 .......................41 
	4-2 實驗一：噪音降低機制 .........................42
	4-3 實驗二：實際系統模擬 .........................46
第5章 未來發展與結論 ........................58
參考文獻 .....................................60

[1] Bernard Widrow and Samuel D.Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1985.

[2] Simon Haykin, Adaptive Filter Theory, 2nd ed., Englewood Cliffs, NJ: Prentice-Hall Inc., 1991.

[3] Marc Moonen and Ian Proudler, An Introduction to Adaptive Signal Processing, course notes available at ESAT website at K.U.Leuven.

[4] Alan V. Oppenheim and Ronald W. Schafer, Digital Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 1975.

[5] John G. Proakis and Vinay K. Ingle, A Self-Study Guide for Digital Signal Processing, Upper Saddle River, NJ: Pearson Education Inc., 2004.

[6] John G. Proakis and Dimitris G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 3rd ed., Prentice-Hall, 1996.


[7] Earl R. Ferrara, “Fast Implementation of LMS Adaptive Filters,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, pp.474-475, Aug. 1980.

[8] S. F. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-27, pp.113-120, Apr. 1979.

[9] M. Dahl and I. Claesson, “Acoustic Noise and Echo Canceling with Microphone Array,” IEEE Transactions on Vehicular Technology, vol.48, No.5, pp.1518-1526, Sep. 1999.

[10] H. Buchner, J. Benesty and W. Kellermann, “An Extended Multidelay Filter: Fast Low-Delay Algorithms for Very High-Order Adaptive Systems” Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Hong Kong, China, Apr. 2003.

[11] Y. Guelou, A. Benamar and P. Scalart, “Analysis of Two Structures for Combined Acoustic Echo Cancellation and Noise Reduction,” Acoustics, Speech, and Signal Processing, vol.2, 1996.