Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.

Enhancement of Wear and Corrosion Resistance of Beta Titanium Alloy by Laser Gas Alloying with Nitrogen

The relatively high elastic modulus coupled with the presence of toxic vanadium (V) in Ti6Al4 V alloy has long been a concern in orthopaedic applications. To solve the problem, a variety of non-toxic and low modulus beta-titanium (beta-Ti) alloys have been developed. Among the beta-Ti alloy family, the quaternary Ti-Nb-Zr-Ta (TNZT) alloys have received the highest attention as a promising replacement for Ti6Al4 V due to their lower elastic modulus and outstanding long term stability against corrosion in biological environments. However, the inferior wear resistance of TNZT is still a problem that must be resolved before commercialising in the orthopaedic market. In this work, a newly-developed laser surface treatment technique was employed to improve the surface properties of Ti-35.3Nb-7.3Zr-5.7Ta alloy. The surface structure and composition of the laser-treated TNZT surface were examined by grazing incidence x-ray diffraction (GI-XRD) and x-ray photoelectron spectroscopy (XPS). The wear and corrosion resistance were evaluated by pin-on-plate sliding test and anodic polarisation test in Hanks’ solution. The experimental results were compared with the untreated (or base) TNZT material. The research findings showed that the laser surface treatment technique reported in this work can effectively improve the wear and corrosion resistance of TNZT.

Gamma-ray Spectra and Enhancement Factors for Positron Annihilation with Core Electrons

Many-body theory is developed to calculate the γ spectra for positron annihilation in noble-gas atoms. Inclusion of electron-positron correlation effects and core annihilation gives spectra in excellent agreement with experiment [K. Iwata et al., Phys. Rev. Lett. 79, 39 (1997)]. The calculated correlation enhancement factors γnl for individual electron orbitals nl are found to scale with the ionization energy I_nl (in eV), as γnl=1+ √A/Inl+(B/I_nl)β, where A≈40  eV, B≈24  eV, and β≈2.3.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.