This study presents a fast adaptive algorithm for noise estimation in non-stationary environments. To make noise estimation adapt quickly to non-stationary noise environments, a robust entropy-based voice activity detection (VAD) is thus required. It is well-known that the entropy-based measure defined in spectral domain is very insensitive to the changing level of nose. To exploit the specific nature of straight lines existing on speech-only spectrogram, the proposed spectrum entropy measurement improved from spectrum entropy proposed by Shen et al. is further presented and is named band-splitting spectrum entropy (BSE). Consequently, the proposed recursive noise estimator including BSE-based VAD can update noise power spectrum accurately even if the noise-level quickly changes.

In this paper, we present a novel beam-former capable of tracking a rapidly moving speaker in a very noisy environment. The localization algorithm extracts a set of candidate direction-of-arrival (DOA) for the signal sources using array signal processing methods in the frequency domain. A minimum variance (MV) beam-former identifies the speech signal DOA in the direction where the signal's spectrum entropy is minimized. A fine tuning process detects the MV direction which is closest to the initial estimation using a smaller analysis window. Extended experiments, carried out in the range of 20-0 dB SNR, show significant improvement in the recognition rate of a moving speaker especially in very low SNRs (from 11.11% to 43.79% at 0 dB SNR in anechoic environment and from 9.9% to 30.51% in reverberant environment).