8 accepted papers
Conventional hard-label and soft-label labeling strategies for Speech Emotion Recognition (SER) fail to capture the diversities in annotator expertise in perceiving complex human emotions. This study introduces novel soft-label approaches that integrate emotion-specific annotator abilities and optim…
Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural r…
Non-invasive and continuous blood pressure (BP) monitoring is essential for the early prevention of many cardiovascular diseases. Estimating arterial blood pressure (ABP) from photoplethysmography (PPG) has emerged as a promising solution. However, existing deep learning approaches for PPG-to-ABP re…
Improving the quality of underwater images is essential for advancing marine research and technology. This work introduces a sparsity-driven interpretable neural network (SINET) for the underwater image enhancement (UIE) task. Unlike pure deep learning methods, our network architecture is based on a…
Seismic Fault Detection is a crucial aspect of oil exploration. While traditional deep learning methods struggle to handle complex seismic data patterns, training a deep learning model solely on synthetic seismic data may not yield satisfactory results. This research paper, involves utilizing a pre-…
Interpreting seismic data involves finding out subsurface geologic information. In seismic data interpretation, one of the crucial steps is to delineate seismic faults. Natural gas and oil reservoirs are more likely to be present where seismic faults exist. In this paper, we develop a graph neural n…
Multimodal speech emotion recognition (MSER) is an emerging and challenging field of research due to its more robust characteristics than unimodal. However, in multimodal approaches, the interactive relations for model building using different modalities of speech representations for emotion recogni…
Many activities such as drilling and exploration in the oil and gas industries rely on identifying seismic faults. Using graph high-frequency components as inputs to a graph convolutional network, we propose a method for detecting faults in seismic data. In Graph Signal Processing (GSP), digital sig…