GMM basedbayesian approach to speech enhancement in signal/transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator.The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Slow Slip Acceleration beneath Andaman Islands Triggered by the 11 April 2012 Indian Ocean Earthquakes

The M-w 8.6 and 8.2 strike-slip earthquakes that struck the northeast Indian Ocean on 11 April 2012 resulted in coseismic deformation both at near and distant sites. The slip distribution, deduced using seismic-wave analysis for the orthogonal faults that ruptured during these earthquakes, is sufficient to predict the coseismic displacements at the Global Positioning System (GPS) sites, such as NTUS, PALK, and CUSV, but fall short at four continuous sites in the Andaman Islands region. Slip modeling, for times prior to the events, suggests that the lower portion of the thrust fault beneath the Andaman Islands has been slipping at least at the rate of 40 cm/yr, in response to the 2004 Sumatra-Andaman coseismic stress change. Modeling of GPS displacements suggests that the en echelon and orthogonal fault ruptures of the 2012 intraplate oceanic earthquakes could have possibly accelerated the ongoing slow slip, along the lower portion of the thrust fault beneath the islands with a month-long slip of 4-10 cm. The misfit to the coseismic GPS displacements along the Andaman Islands could be improved with a better source model, assuming that no local process contributed to this anomaly.