17 accepted papers
Depression and Attention Deficit Hyperactivity Disorder (ADHD) stand out as the common mental health challenges today. In affective computing, speech signals serve as effective biomarkers for mental disorder assessment. Current research, relying on labor-intensive hand-crafted features or simplistic…
Depression significantly affects emotions, thoughts, and daily activities. Recent research indicates that speech signals contain vital cues about depression, sparking interest in audiobased deep-learning methods for estimating its severity. However, most methods rely on time-frequency representation…
With the rapid development of intelligent surveillance, video anomaly detection has become a popular topic in related areas of artificial intelligence. In this work, the main focus is on those applications where the privacy of human targets is concerned, such as outdoor and indoor surveillance and s…
In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules. To be concrete, we firstly transform the raw skeleton sequence into the signal-lev…
Human abnormal activity detection for automatic surveillance systems is to detect abnormal objects and human behaviours in videos. In this paper, we propose to explicitly address different kinds of abnormal events by developing a two-stream fusion approach that integrates both geometry and image tex…
Human Action Recognition and Anomaly Detection significantly improved automatic video analysis, assisted living, and video-based surveillance. The focus of this work is on those applications where privacy protection is required, such as surveillance and assisted living. RGB video data is the most co…
This paper describes work towards a very low-cost medical ultrasound imaging system using concepts of ego-motion estimation. This will enable a B-mode image to be constructed from a very simple probe with a single fixed beam which is either manually scanned across the skin (linear) or rotated agains…
Human behaviour anomaly detection is crucial for modern artifi-cial intelligence systems. However, privacy protection plays a great role in the realization. In this paper, an online privacy-preserving anomaly detector is presented. The proposed method is able to discriminate on human subject body mo…
Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-H…
In this paper, we propose deep discriminative correlation matching within the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for online multiple human tracking. In this matching scheme, we mainly exploit the Convolutional Neural Network (CNN) based Discriminative Correlation Filter…
The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the perfor…
Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propos…
Time-frequency (T-F) masking algorithms are focused at separating multiple sound sources from binaural reverberant speech mixtures. The statistical modelling of binaural cues i.e. interaural phase difference (IPD) and interaural level difference (ILD) is a significant aspect of such algorithms. In t…
In this paper, we propose a novel robust multiple human tracking approach based upon processing a video signal by utilizing a social force model to enhance the particle probability hypothesis density (PHD) filter. In traditional dynamic models, the states of targets are only predicted by their own h…
The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distrib…
A common approach to blind source separation is to use independent component analysis. However when dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in ind…
The model-based expectation maximization source separation and localization (MESSL) technique is a probabilistic time-frequency masking algorithm that achieves underdetermined blind source separation of speech sources. Using only two-channel recordings, MESSL clusters spectrogram points based on the…