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Found 11 papers, 3 papers with code
Enhancing Suicide Risk Assessment: A Speech-Based Automated Approach in Emergency Medicine
The metadata integration yields a balanced accuracy of $94. 4\,\%$, marking an absolute improvement of $28. 2\,\%$, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.
Exploring Meta Information for Audio-based Zero-shot Bird Classification
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research.
Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study
Integrating research on laughter, affect-as-information, and infomediaries' social evaluations of firms, we hypothesize that voiced laughter in executive communication positively affects social approval, defined as audience perceptions of affinity towards an organization.
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and Personalisation
Participants predict the presence of spontaneous humour in a cross-cultural setting.
Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence
We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.
Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
Our findings suggest that for the automatic analysis of humour and its sentiment, facial expressions are most promising, while humour direction can be best modelled via text-based features.
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled with continuous emotion values (arousal and valence) of people in stressful dispositions.
Depression Diagnosis and Forecast based on Mobile Phone Sensor Data
Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states.
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression.
Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet
We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects.
Audio Self-supervised Learning: A Survey
Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
