單一麥克風主動式雜訊消除之新演算法

Abstract

主動式雜訊消除(Active Noise Cancellation,ANC) 為一種利用產生第二 雜訊源和所要去除的雜訊產生破壞性干涉的方法來去除雜訊的一種方法. 傳統的主動式雜訊消除使用多個麥克風來獲得雜訊音場的資料以達到去除 雜訊的目的. 在本篇論文中, 我們只考慮單一麥克風的情況. 將雜訊模擬 成一隨機程序, 並應用一演算法隨時調整此隨機程序的參數. 根據所佔計 得的參數, 來產生另一雜訊源. 雜訊去除和預測為在單一麥克風主動式雜 訊消除的兩大步驟, 但大部份的系統均使用卡門濾波器(Kalman Filter) 來消除雜訊和系統鑑別演算法求得模型參數. 但是, 卡門濾波器涉及太多 的矩陣運算, 不容易以硬體來實現. 在本篇論文中, 我們提出一結合雜訊 去除和預測的演算法,且沒有任何矩陣運算. 模擬的結果指出此演算法和 現有的方法有相同的效果. Active noise cancellation (ANC) is an approach to reduce noise, which produces a secondary noise field to destructively interferes with the unwanted noise. Conventional ANC systems rely on multiple sensors to measure the unwanted noise field and the result of cancellation. This thesis considers a single sensor ANC. The noise field is modeled as a stochastic process, and an algorithm is used to adaptively estimate the parameters of the process. Based on theses estmates, a canceling signal is generated. Filtering and prediction are two operations in single sensor ANC. The existing methods use the Kalman filter to perform filtering operation and system identification algorithms to find system parameters. However, the Kalman filter involves many matrix computations, which is difficult for hardware implementation. In this thesis, we propose a new algorithm for single sensor ANC. Our algorithm can simultaneously perform filtering and prediction and no matrix operations are required. Simulations show that our algorithm can perform almost as good as the existing ones.