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A beam-free approach to channel allocation in a multi-beam
four-color satellite coverage area is taken. Non-Orthogonal
Multiple Access (NOMA) and Orthogonal Multiple Access
(OMA) are compared as methods to serve users nonnecessarily
located on the reference beam. A proportional
fairness policy is employed for the user scheduling. The
naturally occurring SNR imbalances in the user terminal population
are exploited in such a way that NOMA outperforms
OMA, partly due to the blurring of the boundaries of the

Audio codecs are typically transform-domain based and efficiently code stationary audio signals, but they struggle with speech and signals containing dense transient events such as applause. Specifically, with these two classes of signals as examples, we demonstrate a technique for restoring audio from coding noise based on generative adversarial networks (GAN). A primary advantage of the proposed GAN-based coded audio enhancer is that the method operates end-to-end directly on decoded audio samples, eliminating the need to design any manually-crafted frontend.

Early detection of mental fatigue and changes in vigilance could be used to initiate neurostimulation to treat patients suffering from brain injury and mental disorders. In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two non-human primates (NHPs) as they performed a cognitively demanding task over extended periods of time. We employed a set of biomarkers to identify mental fatigue and a gradient boosting classifier to predict the performance outcome, seconds prior to the actual behavior response.

Presentation slides of ICASSP 2020 video

This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently.

In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN).
The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining a vocal tract LP filter with a WaveRNN-based vocal source (i.e., excitation) generator.

We propose a fast algorithm for an adaptive variant of the classical bilateral filter, where the range kernel is allowed to vary from pixel to pixel. Several fast and accurate algorithms have been proposed for bilateral filtering, but they assume that the same range kernel is used at each pixel and hence cannot be used for adaptive bilateral filtering (ABF). Only recently, it was shown that fast algorithms for ABF can be developed by approximating the local histogram around each pixel using polynomials.