Self-Supervised Depression Detection with Time-Frequency Fusion

This research explores a self-supervised approach to depression detection, utilizing time-frequency fusion and multi-domain cross-loss. The ArXiv publication suggests a novel methodology in a significant area of mental health, paving the way for potential advancements in diagnostic tools.