臺灣博碩士論文加值系統

在實際情況下的主動噪音控制系統為雙麥克風的架構，由參考麥克風和誤差麥克風所組成的。在主動噪音控制中參考麥克風不只會量到噪音的部分，也會量測到控制喇叭經過聲管回傳的訊號而引起聲音回授的問題。在一般的FxLMS演算法並未考慮聲音回授補償，因此降低了主動噪音控制系統的性能，更糟的情況可能使得主動噪音控制系統變得不穩定。為了解決聲音回授的問題，本文使用文獻上提出的線上回授路徑鑑別觀念，以及應用最佳可變步階演算法於鑑別器與控制器，來進行線上回授路徑鑑別與對參考麥克風所量測到的訊號進行聲音回授補償，因此提升主動噪音控制系統的性能。為了讓系統有更好的消音性能，本文加入輔助噪音功率調整，用來讓鑑別訊號隨著誤差訊號減小而減小，以降低鑑別訊號對消音性能的影響，並提升整體消音性能。在電腦模擬中與既有的方法進行比較得知本文的方法有較好的消音性能。

In practical case of active noise control (ANC) system structure for the dual microphones, by the reference microphone and the error microphone composed. The reference microphone will pick up a signal from the feedback path of the ANC system, causing the acoustic feedback problem. In general the FxLMS algorithm does not consider the acoustic feedback compensation, thus reducing the active noise control system performance, worse case may make active noise control system becomes unstable. To solve the problem of acoustic feedback, we use literature proposed the concept of online feedback path identifying and applying an optimal variable step size algorithms for identifier and controller, used to online feedback path identification and the reference microphone the measured signals for acoustic feedback compensation, therefore improve the performance of active noise control system. For the system have a better performance, we add auxiliary noise power scheduling, used to identification signal decreases follow the error signal is reduced, identification signals to reduce the impact on performance, and improve overall performance. In the computer simulation are compared with existing methods that the proposed method has better performance.

誌 謝 i
摘 要 ii
ABSTRACT iii
論文目錄 iv
圖目錄 v
表目錄 ix
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 既有的線上回授路徑鑑別方法 4
2.1 主動噪音控制系統描述 4
2.2 既有的線上回授路徑鑑別方法 7
第三章 最佳可變步階演算法與輔助噪音功率調整的回授補償方法 16
3.1 最佳可變步階演算法 16
3.1.1 鑑別器之最佳可變步階 16
3.1.2 控制器之最佳可變步階 21
3.2 線上回授路徑鑑別之主動噪音控制系統的介紹 26
3.2.1應用最佳步階於線上回授路徑鑑別之主動噪音控制系統 26
3.2.2演算法之實現 31
3.3 輔助噪音功率調整的介紹與應用 35
第四章 主動噪音控制電腦模擬 38
4.1 AF-ROVSS-NFxLMS/CE之電腦模擬 45
4.2 AF-ROVSS-NFxLMS/CE-ANPS之電腦模擬 50
4.4 演算法使用不同參數之電腦模擬 57
4.5 應用本文之方法於路徑改變下的電腦模擬 62
4.5.1 第一路徑改變 62
4.5.2 第一路徑與回授路徑改變 66
4.5.3 回授路徑改變 70
4.6 與既有的方法之比較 73
第五章 結論與未來展望 86
5.1 結論 86
5.2未來展望 86
參考文獻 87

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