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Although Intelligent Reflective Surfaces (IRSs) are a cost-effective technology promising high spectral efficiency in future wireless networks, obtaining optimal IRS beamformers is a challenging problem with several practical limitations. Assuming fully-passive, sensing- free IRS operation, we introduce a new data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for sumrate optimization in an IRS-aided downlink setting.

In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more ”wild steganalysis”. First, we propose two algorithms and definitions for a fine experimental protocol with datasets owning ”similar difficulty” and ”similar security”. The ”smart crop 2” algorithm allows the introduction of the Nearly Nested Image Datasets (NNID) that ensure ”a similar difficulty” between various datasets, and a dichotomous research algorithm allows a ”similar security”.

Low frequency personal sound zones can be created by controlling the sound pressure in separate spatially confined regions. The performance of a sound zone system using wireless communication may be degraded due to potential packet losses. In this paper, we propose robust FIR filters for low-frequency sound zone system by incorporating information about the expected packet losses into the design.

More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that

Phonation modes play a vital role in voice quality evaluation and vocal health diagnosis. Existing studies on phonation modes cover feature analysis and classification of vowels, which does not apply to real-life scenarios. In this paper, we define the phonation mode detection (PMD) problem, which entails the prediction of phonation mode labels as well as their onset and offset timestamps.

BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results.

Synthetic human speech signals have become very easy to generate given modern text-to-speech methods. When these signals are shared on social media they are often compressed using the Advanced Audio Coding (AAC) standard. Our goal is to study if a small set of coding metadata contained in the AAC compressed bit stream is sufficient to detect synthetic speech. This would avoid decompressing of the speech signals before analysis. We call our proposed method AAC Synthetic Speech Detection (ASSD).

Accurate pitch estimation in speech signal plays a vital role in several applications. Robust pitch estimation in telephone speech is still a challenge due to the narrow bandwidth of the signal. Electroglottograph (EGG) signal is a reliable means for pitch estimation, however, it’s not practically possible to