Fast Start-Up Algorithm for Adaptive Noise Cancellers with Novel SNR Estimation and Stepsize Control

Abstract

This paper proposes a fast convergence algorithm for adaptive noise cancellers with novel SNR (signal-to-noise ratio) estimation and stepsize control. The stepsize for coefficient adaptation is controlled with an estimated SNR for low distortion of the output signal and a small residual noise. A first SNR estimate, defined as a ratio of the noise-canceller output to the reference input, enables quick coefficient growth in the initial adaptation stage. A second SNR estimate, which replaces the reference input in the denominator of the first SNR estimates with the noise replica, achieves low distortion in the output and small residual noise simultaneously. Switchover from the first to the second SNR estimate takes place when the latter becomes comparable to the former. Evaluations with clean speech and noise recorded at a busy station demonstrate that the time until SNR-estimate switchover is reduced by 91% at SNR=0dB compared to the conventional algorithm, leading to fast evolution of adaptive filter coefficients.