Multiframe maximum a posteriori estimators for single-microphone speech enhancement

Multiframe maximum a posteriori (MAP) estimators are applied to a single-microphone noise reduction problem. Several attempts have been made to exploit the interframe correlation (IFC) between speech coefficients in the short-time Fourier transform domain. In a noise-reduction algorithm, all available information of recorded signals should be optimally utilized in the estimation process. Single-microphone multiframe minimum variance distortion-less response and single-microphone multiframe Wiener filters (MFWFs) have been presented in this approach. Incorporating the concept of IFC in the MAP estimator leads to multiframe MAP estimators in a single-microphone case. In each time-frequency unit, the current and a finite number of past noisy signals are utilized to develop the estimators. A complex factor is adopted to model the IFC between speech signals, which allows the application of multiframe MAP estimators. The noise reduction performance is compared for the proposed estimators with the joint MAP estimator (which ignores the correlation between successive frames) and benchmark MFWFs and speech-distortion weighted interframe Wiener filters for different input noise types. These evaluations verify that the proposed methods exhibit good performance.