Simulating a dual beam combiner at SUSI for narrow-angle astrometry

The Sydney University Stellar Interferometer (SUSI) has two beam combiners, i.e. the Precision Astronomical Visible Observations (PAVO) and the Microarcsecond University of Sydney Companion Astrometry (MUSCA). The primary beam combiner, PAVO, can be operated independently and is typically used to measure properties of binary stars of less than 50 milliarc- sec (mas) separation and the angular diameters of single stars. On the other hand, MUSCA was recently installed and must be used in tandem with the for- mer. It is dedicated for microarcsecond precision narrow-angle astrometry of close binary stars. The performance evaluation and development of the data reduction pipeline for the new setup was assisted by an in-house computer simulation tool developed for this and related purposes. This paper describes the framework of the simulation tool, simulations carried out to evaluate the performance of each beam combiner and the expected astrometric precision of the dual beam combiner setup, both at SUSI and possible future sites.Comment: 28 pages, 23 figures, accepted for publication in Experimental Astronomy. The final publication is available at http://link.springer.co

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Distributed Multichannel Speech Enhancement with Minimum Mean-square Error Short-time Spectral Amplitude, Log-spectral Amplitude, and Spectral Phase Estimation

In this paper, the authors present optimal multichannel frequency domain estimators for minimum mean-square error (MMSE) short-time spectral amplitude (STSA), log-spectral amplitude (LSA), and spectral phase estimation in a widely distributed microphone configuration. The estimators utilize Rayleigh and Gaussian statistical models for the speech prior and noise likelihood with a diffuse noise field for the surrounding environment. Based on the Signal-to-Noise Ratio (SNR) and Segmental Signal-to-Noise Ratio (SSNR) along with the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) as objective metrics, the multichannel LSA estimator decreases background noise and speech distortion and increases speech quality compared to the baseline single channel STSA and LSA estimators, where the optimal multichannel spectral phase estimator serves as a significant quantity to the improvements, and demonstrates robustness due to time alignment and attenuation factor estimation. Overall, the optimal distributed microphone spectral estimators show strong results in noisy environments with application to many consumer, industrial, and military products

Post processing of differential images for direct extrasolar planet detection from the ground

The direct imaging from the ground of extrasolar planets has become today a major astronomical and biological focus. This kind of imaging requires simultaneously the use of a dedicated high performance Adaptive Optics [AO] system and a differential imaging camera in order to cancel out the flux coming from the star. In addition, the use of sophisticated post-processing techniques is mandatory to achieve the ultimate detection performance required. In the framework of the SPHERE project, we present here the development of a new technique, based on Maximum A Posteriori [MAP] approach, able to estimate parameters of a faint companion in the vicinity of a bright star, using the multi-wavelength images, the AO closed-loop data as well as some knowledge on non-common path and differential aberrations. Simulation results show a 10^-5 detectivity at 5sigma for angular separation around 15lambda/D with only two images.Comment: 12 pages, 6 figures, This paper will be published in the proceedings of the conference Advances in Adaptive Optics (SPIE 6272), part of SPIE's Astronomical Telescopes & Instrumentation, 24-31 May 2006, Orlando, F