A trade-off between convergence speed and misadjustment for filtering discontinuous speech signals

In this paper we propose a novel LMS algorithm in combination with a voice activity detector (VAD) for filtering speech sounds in the Adaptive Noise Cancelation (ANC) problem. The filtering stage is based on the minimization of the squared Euclidean norm of the difference weight vector under a stability constraint over the a posteriori estimation error. To this purpose, the Lagrangian methodology has been used in order to propose a non-linear adaptation defined in terms of the product of differential inputs and errors. This approach yields better tracking ability under conditions held in Discontinuous Transmission (DTX) systems than previous approaches. In addition the use of a precise VAD provides two operation modes in order to obtain the best trade-off between misadjustment and convergence speed in speech/non-speech frames. The experimental analysis carried out on the AURORA 3 speech databases provides an extensive performance evaluation together with an exhaustive comparison to standard LMS algorithms including the normalized (N)-LMS, and other recently reported LMS algorithms such as the Modified (M)-NLMS or the Normalized Data Nonlinearity (NDN)-LMS Adaptation.