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Binaural cues are important for sound localization. In addition, spatially separated sound sources are more intelligible than when they are co-located. Binaural cue preservation in multi-microphone hearing assistive devices is therefore important for the user's listening experience and safety.
A number of linearly-constrained-minimum-variance (LCMV) based methods

Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods (mostly matrix factorization) are aimed at detecting neurons in the field-of-view and then inferring the corresponding time-traces. In this paper, we reverse the modeling and instead aim to minimize the spatial inference, while focusing on finding the set of temporal traces present in the data.

In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

We propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics in RGB domain. Besides, since semantic map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression.

The conversion of an algorithm to fixed-point arithmetic is commonly achieved with a large and fixed-number of simulations. Nevertheless, when simulating a fixed and ar- bitrary large number of samples, no confidence information is given on the characterization, and this method is often time-inefficient. To overcome this limitation, we propose a new method for noise evaluation. The error induced by fixed-point coding is statistically characterized to compute the noise power with an adaptive and reduced number of simulations.

The energy efficiency of modern MPSoCs is enhanced by complex hardware features such as Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM). This paper introduces a new method, based on convex problem solving, that determines the most energy efficient operating point in terms of frequency and number of active cores in an MPSoC. The solution can challenge the popular approaches based on never-idle (or As-Slow-As-Possible (ASAP)) and race-to-idle (or As-Fast-As-Possible (AFAP)) principles.

An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them.

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding.

Convolutional neural network (CNN) can be applied in glaucoma detection for achieving good performance.
However, its performance depends on the availability of a large number of the labelled samples for its training phase.
To solve this problem, this paper present a semi-supervised transfer learning CNN model for automatic glaucoma detection based on both labeled and unlabeled data.
First, a pre-trained CNN from non-medical data is fine-tuned and trained in a supervised fashion using the labeled data.