對LMS自適應(yīng)算法進行了詳細的性能分析與討論，針對LMS算法運算較復(fù)雜、適應(yīng)性較弱、穩(wěn)定性差的缺點提出了一種HLMS（混合LMS）算法。建立了自適應(yīng)噪聲抵消系統(tǒng)，利用MATILAB軟件對食堂、體育館兩處的錄音信號進行計算機語音去噪仿真分析。實驗結(jié)果表明，兩種自適應(yīng)方法均能有效抑制各種噪聲污染，提高語音信噪比為60%~85%，而且對引入的語音信號失真也較小，二者均在10%以下。HLMS算法比LMS算法更簡潔，LMS算法調(diào)節(jié)性能敏感于參考輸入的方差，而HLMS算法敏感于參考輸入的均方根值，因此HLMS算法的適應(yīng)性比LMS算法更強，在非平穩(wěn)隨機輸入情況下HLMS算法更能提供穩(wěn)定的工作性能，更能較好的恢復(fù)原始語音信號。

Abstract:

?This article analysed and discussed the performance of the LSM adaptive algorithm. Aiming at the LMS algorithm?蒺s shortcomings， such as complex algorithm， weak adaptability， and poor stability etc， it proposed a HLMS （Hybrid LMS） algorithm， established an adaptive noise canceling system， and had the speech denoising simulation of two voice recording signal on the canteens， stadium using MATILAB software by computer. Experimental results show that the two adaptive methods can effectively suppress a variety of noise pollution， and improve the voice signal to noise ratio of 60% to 85%. And the introduction of the voice signal distortion is also smaller， both of them are below 10%. HLMS algorithm is more concise than LMS algorithm. LMS algorithm’s adjusting performance is sensitive to the variance of the reference input， while HLMS algorithm is sensitive to the RMS value of the reference input. So the adaptation of HLMS algorithm is better than the LMS algorithm. In case of non-stationary random input， the HLMS algorithm can provide more stable performance and better recovery of the original speech signal.