本論文主要是使用一新穎的遞迴模糊類神經濾波器(ARNFF)對吵雜的環境中做語音增強的處理，在語音增強的架構中包含兩個麥克風，一為接收主要音源；另一個接收參考輸入音源，並使用ARNFF濾波器衰減主噪音源路徑中的噪音。ARNFF 為六層連接網路，可以輕易的將網路架構轉換成一組動態模糊法則和狀態空間方程式。其學習演算法主要分為兩個部份—架構學習和參數學習演算法，架構學習部分是使用MCA群組化演算法自動建構遞迴模糊類神經網路架構，並得到最精簡的網路架構；參數學習則使用遞迴回歸學習演算法調整系統參數，取得系統的動態行為。在模擬的部份，與一些現存的濾波器做比較，驗證ARNFF對語音增強的處理能力有其優越的效果。本論文使用遞迴模糊類神經濾波器，針對吵雜環境之語音增強具有下列三優點：(1) ARNFF濾波器的建構可以經由MCA學習演算法得到最精簡的濾波器架構；(2) ARNFF濾波器設計時，不需要事先設定精確的濾波器輸入個數；(3) 使用遞迴回歸學習演算法之ARNFF濾波器在延遲較長的環境中做噪音消除具有優越的表現。

　　This study developed a novel adaptive recurrent neuro-fuzzy filter (ARNFF) for speech enhancement in noisy environment. The speech enhancement scheme consists of two microphones that receive a primary and a reference input source respectively, and the ARNFF that attenuates the noise corrupting the original speech signal in the primary channel. The ARNFF is inherently a recurrent multilayered connectionist network that can be translated effortlessly into a set of dynamic fuzzy rules and state-space equations as well. An effective learning algorithm, consisting of a clustering algorithm for the structure learning and a recurrent learning algorithm for the parameter learning, has been developed for the ARNFF structure construction. From our computer simulations and comparisons with some existing filters, the advantages of the proposed ARNFF for noisy speech enhancement include: 1) a more compact filter structure, 2) no a priori knowledge needed for the exact lagged order of the input variables, 3) a better performance in long-delay environment.

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