11 accepted papers
Data imbalance is an important issue in data-driven deep-learning methodologies. In sound event detection (SED), there are two types of data imbalance issues caused by the diverse time duration of sound events: the data imbalance between sound event classes (inter-class imbalance) and the active/ina…
Beamforming weights prediction via deep neural networks has been one of the main methods in multi-channel speech enhancement tasks. The spectral-spatial cues are crucial in beamforming weights estimation, however, many existing works fail to optimally predict the beamforming weights with an absence…
The detection of abnormal fetal heartbeats during pregnancy is important for monitoring the health conditions of the fetus. While adult ECG has made several advances in modern medicine, noninvasive fetal electrocardiography (FECG) remains a great challenge. In this paper, we introduce a new method b…
A differential approach is proposed for tomographic rain field reconstruction using the estimated signal-to-noise ratio of microwave signals from low earth orbit satellites at the ground receivers, with the unknown baseline values eliminated before using least squares to reconstruct the attenuation…
The attenuation of microwave signals can be used for meteorological observations. For example, the received signal level (RSL) of backhaul links of cellular systems, which usually has a quantization error of 0.1 dB or more for commercial systems, has been used to measure rainfall. In this work, thro…
Simple but effective strategies for an undergraduate introductory course in signals and systems are described in this paper. These include peer facilitated tutorials, optional class tests, in-class only lab assessment and use of interactive animations. Peer facilitated tutorials were designed to sup…
Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt the multi-label classification technique to perform the polyphonic acoustic event detection with a global threshold to d…
This paper introduces the use of local binary patterns (LBP) extracted from a time-frequency representation (TFR) for acoustic scene classification. As LBP provides a description of the global TFR texture we propose a novel zoning mechanism that provides a simple solution to extract spectrally relev…
Motivated by the recent progresses in the use of deep learning techniques for acoustic speech recognition, we present in this paper a visual deep bottleneck feature (DBNF) learning scheme using a stacked auto-encoder combined with other techniques. Experimental results show that our proposed deep fe…
This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM). Unlike all existing visual feature extraction techniques which solely extracts features from video sequences, our method is able to explore both acoustic information and visual info…
Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes. We propose to solve both of these problems in a joint framework using RGBD images of indoor scenes. In contrast to recent approaches which focus on either one of these two problems, we…