As a common mental disorder, depression becomes a major threat to human health and may even heavily influence one’s daily life. Considering this background, it is necessary to investigate strategies for automatically detecting depression, especially through the audio modality represented by speech segments, mainly due to the efficient latent information included in speech when describing depression. However, most of the existing works focus on stacking deep networks in audio-based depression detection, which may lead to insufficient knowledge for representing depression in speech. In this regard, we propose a deep learning model based on a parallel convolutional neural network and a transformer in order to mine effective information with an acceptable complexity. The proposed approach consists of a parallel convolutional neural network (parallel-CNN) module used to focus on local knowledge, while a transformer module is employed as the other parallel stream to perceive temporal sequential information using linear attention mechanisms with kernel functions. Then, we performed experiments on two datasets of Distress Analysis Interview Corpus-Wizard of OZ (DAIC-WOZ) and Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). The experimental results indicate that the proposed approach achieves a better performance compared with the state-of-the-art strategies.

In this letter, we present an efficient algorithm for the removal of random-valued impulse noise from a corrupted image by using a reference image. The proposed method uses a statistic of rank-ordered relative differences to identify pixels which are likely to be corrupted by impulse noise. Once a noisy pixel is identified, its value is restored by a simple weighted mean filter. Simulation results indicate that our algorithm provides a significant improvement over many other existing techniques.

Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the inherent information from speech. To focus on more emotionally salient regions of depression speech, in this research, we propose a multi-head time-dimension attention-based long short-term memory (LSTM) model. We first extract frame-level features to store the original temporal relationship of a speech sequence and then analyze their difference between speeches of depression and those of health status. Then, we study the performance of various features and use a modified feature set as the input of the LSTM layer. Instead of using the output of the traditional LSTM, multi-head time-dimension attention is employed to obtain more key time information related to depression detection by projecting the output into different subspaces. The experimental results show the proposed model leads to improvements of 2.3 and 10.3% over the LSTM model on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) corpus, respectively.

Speech Emotion Recognition (SER) is a powerful tool for endowing computers with the capacity to process information about the affective states of users in human-machine interactions. Recent research has shown the effectiveness of graph embedding based subspace learning and extreme learning machine applied to SER, but there are still various drawbacks in these two techniques that limit their application. Regarding subspace learning, the change from linearity to nonlinearity is usually achieved through kernelisation, while extreme learning machines only take label information into consideration at the output layer. In order to overcome these drawbacks, this paper leverages extreme learning machine for dimensionality reduction and proposes a novel framework to combine spectral regression based subspace learning and extreme learning machine. The proposed framework contains three stages - data mapping, graph decomposition, and regression. At the data mapping stage, various mapping strategies provide different views of the samples. At the graph decomposition stage, specifically designed embedding graphs provide a possibility to better represent the structure of data, through generating virtual coordinates. Finally, at the regression stage, dimension-reduced mappings are achieved by connecting the virtual coordinates and data mapping. Using this framework, we propose several novel dimensionality reduction algorithms, apply them to SER tasks, and compare their performance to relevant state-of-the-art methods. Our results on several paralinguistic corpora show that our proposed techniques lead to significant improvements.

This letter proposes a broadband high isolation and high power compact single pole double throw (SPDT) switch. In contrast to the traditional structure of grounded source pad, the source pad of the shunt-stacked FET is absorbed into the transmission line, and the drain of the shunt-stacked FET is grounded to a via-hole. This structure helps to extend the operation bandwidth and reduce the chip size. The proposed SPDT switch is composed of six shunt-stacked FET units on each branch with this novel structure. Stacked FETs technique increases the voltage handling by placing two FETs in series. It has been fabricated using a commercial 0.1 μm GaAs pseudomorphic high-electron-mobility transistor (PHEMT) process. The chip size is 1.2x0.8 mm². The switch operates at the frequency range of 35-70 GHz with less than 3 dB insertion loss and more than 40-dB isolation. The switch demonstrates a 1 dB insertion loss compression with a 20.2 dBm input power at 31 GHz. The isolation is larger than 30 dB from 10 to 95 GHz. To the best of the authors' knowledge, this broadband isolation performance is among the best of the reported SPDT switches.