藉小波包分頻NLMS之回音消除

Abstract

當輸入信號對於雜訊很小時,NLMS會有穩定度之問題產生.干擾 係 數法被用來解決此問題.我們提出改善的方法來增加其效能。由於 房間 殘響的關係,適應性回音消除器的階數幾千個通常是需要的.這將 導致慢 的收斂速度和運算複雜度高的問題.本論文利用小波包分頻NLMS來解決運 算複雜問題.但由於減少取樣頻率會產生重疊現象,需要額外 交叉項濾波 器,導致收斂速度變慢.藉著分頻NLMS改善的方法來增加收 斂速度. The normalized LMS is widely used because of its simplicity. However, the algorithm behaves unstably when all elements of an input vector is very small with respect to noise power. Coefficients of the adaptive filter are not updated when a norm of an input vector is smaller than a threshold. But we proposed an improved method that coefficients are updated by a smaller stepsize if this condition happens. Due to the long reverberation time of a room, it is necessary to use filters of several thousands taps.This result causes slow convergence speed and high computational complexity. In the thesis, a subband NLMS based on wavelet packets can overcome the difficulty. Because of the downsampling process causes aliasing, adjacent band adaptive filters are proposed in order to eliminate it. But adjacent band filters result in slow convergence, we come up with the improved refinement iteration to speed up convergence speed.