WEMAC: Women and Emotion Multi-modal Affective Computing dataset.

WEMAC is a unique open multi-modal dataset that comprises physiological, speech, and self-reported emotional data records of 100 women, targeting Gender-based Violence detection. Emotions were elicited through visualizing a validated video set using an immersive virtual reality headset. The physiological signals captured during the experiment include blood volume pulse, galvanic skin response, and skin temperature.

Ecology & computer audition: Applications of audio technology to monitor organisms and environment.

Among the 17 Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change. Moreover, SDGs 14 and 15 claim the protection and conservation of life below water and life on land, respectively. In this work, we provide a literature-founded overview of application areas, in which computer audition - a powerful but in this context so far hardly considered technology, combining audio signal processing and machine intelligence - is employed to monitor our ecosystem with the potential to identify ecologically critical processes or states.

Fatigue Prediction in Outdoor Running Conditions using Audio Data.

Although running is a common leisure activity and a core training regiment for several athletes, between 29% and 79% of runners sustain an overuse injury each year. These injuries are linked to excessive fatigue, which alters how someone runs. In this work, we explore the feasibility of modelling the Borg received perception of exertion (RPE) scale (range: [6]-[19] [20]), a well-validated subjective measure of fatigue, using audio data captured in realistic outdoor environments via smartphones attached to the runners' arms.