Correlating subword articulation with lip shapes for embedding aware audio-visual speech enhancement.

In this paper, we propose a visual embedding approach to improve embedding aware speech enhancement (EASE) by synchronizing visual lip frames at the phone and place of articulation levels. We first extract visual embedding from lip frames using a pre-trained phone or articulation place recognizer for visual-only EASE (VEASE). Next, we extract audio-visual embedding from noisy speech and lip frames in an information intersection manner, utilizing a complementarity of audio and visual features for multi-modal EASE (MEASE).

A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films.

We propose using faster regions with convolutional neural network features (faster R-CNN) in the TensorFlow tool package to detect and number teeth in dental periapical films. To improve detection precisions, we propose three post-processing techniques to supplement the baseline faster R-CNN according to certain prior domain knowledge. First, a filtering algorithm is constructed to delete overlapping boxes detected by faster R-CNN associated with the same tooth.

Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

Objective: We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients.

Design: The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach.

Parametric Dependencies of Sliding Window Correlation.

Objective: In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases.

Adaptive change point detection of dynamic functional connectivity networks.

This study presents a new algorithm to adaptively detect change points of functional connectivity networks in the brain. It uses scans from resting-state functional magnetic resonance imaging (rsfMRI) which is one of the major tools to investigate intrinsic brain functionality. Different regions of the resting brain form networks that change states within a few seconds to minutes.

A Deep Denoising Autoencoder Approach to Improving the Intelligibility of Vocoded Speech in Cochlear Implant Simulation.

Objective: In a cochlear implant (CI) speech processor, noise reduction (NR) is a critical component for enabling CI users to attain improved speech perception under noisy conditions. Identifying an effective NR approach has long been a key topic in CI research.

Method: Recently, a deep denoising autoencoder (DDAE) based NR approach was proposed and shown to be effective in restoring clean speech from noisy observations.

Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states.

A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a "gold standard" for comparison, evaluating the performance of the SWC in typical resting-state data is challenging.

Cluster-based analysis for characterizing dynamic functional connectivity.

Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC.

A single-ensemble-based hybrid approach to clutter rejection combining bilinear Hankel with regression.

Purpose: Clutter regarded as ultrasound Doppler echoes of soft tissue interferes with the primary objective of color flow imaging (CFI): measurement and display of blood flow. Multi-ensemble samples based clutter filters degrade resolution or frame rate of CFI. The prevalent single-ensemble clutter rejection filter is based on a single rejection criterion and fails to achieve a high accuracy for estimating both the low- and high-velocity blood flow components.

A ridge ensemble empirical mode decomposition approach to clutter rejection for ultrasound color flow imaging.

In color flow imaging, it is a challenging work to accurately extract blood flow information from ultrasound Doppler echoes dominated by the strong clutter components. In this paper, we provide an in-depth analysis of ridge ensemble empirical mode decomposition (R-EEMD) and compare it with the conventional empirical mode decomposition (EMD) framework. R-EEMD facilitates nonuniform and trial-dependent weights obtained by an optimization procedure during ensemble combination and results in less decomposition errors when compared with the conventional ensemble empirical mode decomposition techniques.