4 papers
Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition
Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker +1
Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild l…
Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations
Ioannis Ziogas, Ensieh Khazaei, Bilal Taha +4
Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing ap…
Variational decomposition autoencoding improves disentanglement of latent representations
Ioannis Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker +1
Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech an…
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition
Ioannis Ziogas, Hessa Alfalahi, Ahsan H. Khandoker +1
Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeli…