Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Detecting Improvised Explosive Devices Via Forward Looking Ground Penetrating Radar

Forward-looking ground penetrating radar shows promise for detection of improvised explosive devices in active war zones. Because of certain insurmountable physical limitations, post-processing algorithm development is the most popular research topic in this field. One such investigative avenue explores the worthiness of frequency analysis during data post-processing. Using the finite difference time domain numerical method, simulations are run to test both mine and clutter frequency response. Mines are found to respond strongest at low frequencies and cause periodic changes in ground penetrating radar frequency results. These results are called into question, however, when clutter, a phenomenon generally known to be random, is also found to cause periodic frequency effects. Possible causes, including simulation inaccuracy, are considered. Although the clutter models used are found to be inadequately random, specular reflections of differing periodicity are found to return from both the mine and the ground. The presence of these specular reflections offers a potential alternative method of determining a mine’s presence.