82 citations · 129 across the 12 of their papers we have counts for
12 papers
A Deep Matched Filter For R-Peak Detection in Ear-ECG
Harry J. Davies, Ghena Hammour, Marek Zylinski +2
The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity using electrodes that can be embedded within earpho…
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning
Yuyang Miao, Harry J. Davies, Danilo P. Mandic
Photoplethysmography (PPG) refers to the measurement of variations in blood volume using light and is a feature of most wearable devices. The PPG signals provide insight into the b…
Relating EEG recordings to speech using envelope tracking and the speech-FFR
Mike Thornton, Danilo Mandic, Tobias Reichenbach
During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and atte…
Hearables: Ear EEG Based Driver Fatigue Detection
Metin C. Yarici, Pierluigi Amadori, Harry Davies +4
Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which…
Hearables: Feasibility of Recording Cardiac Rhythms from Single Ear Locations
Metin Yarici, Wilhelm Von Rosenberg, Ghena Hammour +5
Wearable technologies are envisaged to provide critical support to future healthcare systems. Hearables - devices worn in the ear - are of particular interest due to their ability…
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
Haohe Liu, Zehua Chen, Yi Yuan +5
Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited gener…
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Zehua Chen, Yihan Wu, Yichong Leng +9
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, the…
Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach
Harry J. Davies, Danilo P. Mandic
Much of the information of breathing is contained within the photoplethysmography (PPG) signal, through changes in venous blood flow, heart rate and stroke volume. We aim to levera…
Demystifying CNNs for Images by Matched Filters
Shengxi Li, Xinyi Zhao, Ljubisa Stankovic +1
The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era. Despite success, CNNs have been cons…
Ear-EEG Sensitivity Modelling for Neural and Artifact Sources
Metin Yarici, Mike Thornton, Danilo Mandic
The ear-EEG has emerged as a promising candidate for wearable brain monitoring in real-world scenarios. While experimental studies have validated ear-EEG in multiple scenarios, the…
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Yichong Leng, Zehua Chen, Junliang Guo +9
Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing the…
InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training
Zehua Chen, Xu Tan, Ke Wang +4
Denoising diffusion probabilistic models (diffusion models for short) require a large number of iterations in inference to achieve the generation quality that matches or surpasses…