臺灣博碩士論文加值系統

雙麥克風架構的主動噪音控制系統實際應用時，參考麥克風不僅會量測到噪音源訊號，也會量測到控制喇叭回傳的控制音，所以會產生聲音回授的現象，並且在系統穩態時會凸顯白色雜訊的影響。因此，為了解決聲音回授與白色雜訊的影響，本篇論文提出VSS-NFxLMS/CE-ANEDP演算法來鑑別回授路徑與補償聲音回授訊號並降低白色雜訊對於系統的影響。本文在控制器演算法中應用可變步階NFxLMS演算法，來提升控制器應付白色雜訊與聲音回授影響的能力。本文提出的方法在電腦模擬中可知，加入指數遞減的輔助噪音(ANEDP)不僅可以提升回授鑑別準確度，也提升系統的消音性能，因此本文的方法有著良好的回授路徑鑑別準確度與消音性能；與既有的方法比較之下，本文提出的方法有較準確的回授路徑鑑別與較快的系統收斂速度，並保有良好的消音性能。

The practical application of dual microphones active noise control system structure, the reference microphone will not only measure noise source signal, but also measure return control sound by the control speaker, so it will generate the phenomenon of acoustic feedback, and highlight the impact of white noise in steady-state system. Therefore, in order to solve these problems, this paper proposes VSS-NFxLMS/CE-ANEDP algorithm to model feedback path to solve the effects of acoustic feedback and reduce the impact of white noise for the system. In this paper , a variable step-size NFxLMS algorithm in the controller to enhance the ability of the controller to cope with impact of white noise and the acoustic feedback. The proposed method in the computer simulation shows that adding an auxiliary noise of exponential-decay power(ANEDP) can not only enhance the accurate identification of the feedback path, but also enhance the performance of the system, thus this method has a good identification of the acoustic feedback path accuracy and silencer performance. Compared with the existing methods, the proposed method is more accurate identification of the feedback path and faster convergence speed of the system, and maintain good silencing effect.

誌 謝 i
摘要 ii
ABSTRACT iii
論文目錄 iv
圖目錄 v
表目錄 vii
第一章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 1
1.3 論文概要 3
第二章 聲音回授影響與既有的回授路徑鑑別方法 4
2.1 主動噪音控制系統描述 4
2.2 聲音回授影響分析與離線的聲音回授補償方法 7
2.2.1 聲音回授影響分析 7
2.2.2 離線的聲音回授補償方法 8
2.3 既有的線上聲音回授補償方法 10
第三章 應用具有指數遞減功率的輔助噪音於線上聲音回授路徑的鑑別 20
3.1 應用可變步階於線上回授路徑鑑別 21
3.1.1 輔助鑑別器與鑑別器之演算法推導 21
3.1.2 控制器之可變步階推導 24
3.1.3 控制器演算法之實現 27
3.2 指數遞減功率的輔助噪音之介紹 30
第四章 主動噪音控制電腦模擬 31
4.1 本文提出的方法與無ANEDP的方法之比較 37
4.2 使用不同的參數之電腦模擬 45
4.3 與既有的方法之比較 55
第五章 結論與未來展望 62
5.1 結論 62
5.2 未來展望 62
參考文獻 63

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