6 accepted papers
Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-thewild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through grap…
Signal decomposition techniques aim to break down non-stationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A well-…
While machines struggle to cope with acoustical variability and noise, humans show remarkable robustness to recognize speech content under different conditions of environmental noise. The tonotopic organization of the spiral human cochlea has motivated the signal processing community for its superb…
Signal multiscale decomposition (SMD) is an effective analysis for the identification of modal information in time-domain signals. So far, various SMD approaches, such as the Multiresolution Wavelet Transform (MWT), the Empirical Mode Decomposition (EMD), and the Variational Mode Decomosition (VMD)…
The current study aims at creating an algorithm able to detect Coronary Artery Disease (CAD), using ultrashort (duration of 30 seconds) one-lead ECG recordings. The presented method is designed to allow both electrode and noisy recordings (deriving from a smartwatch) as input. This is achieved by us…
The variational mode decomposition (VMD) is a widely applied optimization-based method, which analyzes non-stationary signals concurrently. Correspondingly, its recently proposed multivariate extension, i.e., MVMD, has shown great potentials in analyzing multichannel signals. However, the requiremen…