ML Estimation and CRBs for Reverberation, Speech and Noise PSDs in Rank-Deficient Noise-Field
This work addresses a practical issue in speech communication systems for scenarios with multiple directional noise sources, though it appears incremental as it builds on existing estimation frameworks.
The paper tackles the problem of estimating speech, reverberation, and noise power spectral densities (PSDs) in acoustic environments with rank-deficient noise, deriving two closed-form maximum likelihood estimators and Cramer-Rao bounds, with simulations and real signals showing advantages over competing methods.
Speech communication systems are prone to performance degradation in reverberant and noisy acoustic environments. Dereverberation and noise reduction algorithms typically require several model parameters, e.g. the speech, reverberation and noise power spectral densities (PSDs). A commonly used assumption is that the noise PSD matrix is known. However, in practical acoustic scenarios, the noise PSD matrix is unknown and should be estimated along with the speech and reverberation PSDs. In this paper, we consider the case of rank-deficient noise PSD matrix, which arises when the noise signal consists of multiple directional interference sources, whose number is less than the number of microphones. We derive two closed-form maximum likelihood estimators (MLEs). The first is a non-blocking-based estimator which jointly estimates the speech, reverberation and noise PSDs, and the second is a blocking-based estimator, which first blocks the speech signal and then jointly estimates the reverberation and noise PSDs. Both estimators are analytically compared and analyzed, and mean square errors (MSEs) expressions are derived. Furthermore, Cramer-Rao Bounds (CRBs) on the estimated PSDs are derived. The proposed estimators are examined using both simulation and real reverberant and noisy signals, demonstrating the advantage of the proposed method compared to competing estimators.