Analysis of the Decision-Directed SNR Estimator for Speech Enhancement With Respect to Low-SNR and Transient Conditions

Because of their many applications and their relative ease of implementation, single-channel speech enhancement algorithms have received much attention. As a consequence, a vast amount of publications on estimation procedures and their implementation in noise reduction systems exists. However, there has been little systematic research on the theoretic performance of such estimators. In this paper, we provide a systematic analysis of the performance of noise reduction algorithms in low signal-to-noise ratio (SNR) and transient conditions, where we consider approaches using the well-known decision-directed SNR estimator. We show that the smoothing properties of the decision-directed SNR estimator in low SNR conditions can be analytically described and that the limits of noise reduction for widely used spectral speech estimators based on the decision-directed approach can be predicted. We also illustrate that achieving both a good preservation of speech onsets in transient conditions on one side and the suppression of musical noise on the other can be especially problematic when the decision-directed SNR estimation is used.