Adaptive Noise Canceller Algorithm with SNR-Based Stepsize and Data-Dependent Averaging

Abstract

This paper proposes an adaptive noise canceller algorithm with an SNR-based stepsize and data-dependent averaging. A first and a second SNR with different time constants for averaging are used one after the other to control the stepsize in adaptation. The time constants reflect the accuracy of the SNR estimates such that lower accuracy is offset with a larger time constant. The first SNR estimate with lower accuracy guarantees the initial coefficient growth whereas the second SNR estimate provides more accurate adaptation control. Switchover from the first to the second SNR estimate takes place when the coefficient growth is saturated. Evaluations with clean speech and noise recorded at a busy station demonstrate that the coefficient error by the proposed algorithm is as much as 6dB smaller than that by conventional algorithms with a constant averaging. It is a counterintuitive result that averaging a first estimate does not always make it better.