The main task of a noise PSD tracker ist estimation of the current noise PSD alone from the instantaneous STFT magnitudes of noisy signal. The challenging task of noise PSD estimation in the presence of non-stationary noise has spurred the development of many sophisticated algorithms during the last years. A closer look into their functionality shows that they can be categorized along the following five techniques:
- Because of the sparseness of the clean speech PSD some noise trackers make use of a minimum search (Min.-Search) of the noisy PSD over a certain number of the previous frames, which are closely related to the desired noise PSD estimate.
- Other approaches employ a voice activity detection (VAD) or a speech presence probability (SPP) estimation, again exploiting the sparseness of speech, to find the noise-only time-frequency slots where the noise PSD estimate can be updated.
- Due to the random nature of the signals, they are often modeled as realizations of random processes with given probability density function (PDF) enabling e.g. an analytical bias compensation (Bias-Comp.) of the noise PSD estimates.
- Furthermore, the statistical modeling facilitates a Bayesian inference (Bayes-Inf.) such as the minimum mean squared error (MMSE) estimators for noise PSD.
- Since the STFT coefficients of the noise signals are correlated in a certain neighbourhood even for white noise, an output smoothing (Out.-Smooth.) becomes another very popular technique in the noise tracking.
A mandatory property of all approaches for noise PSD estimation, when used in communication scenarios, is its causality.
Techniques used in state-of-the-art noise PSD estimators
|1. MS, BSMS|| x|| x|
|2. VAD-RA, SPP-FT|| x|| x|
|3. MCRA-based|| x|| x|| x|
|4. IMCRA|| x|| x|| x|| x|
|5. MMSE-SPP|| x|| x|| x|
|6. MMSE-BM|| x|| x|| x|| x|
Developing of robust causal noise PSD trackers is still a challenging and exciting task in modern research.