UC3M4Safety Database - WEMAC: Audio features
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.Audio signals collected during laboratory experiment to measure participants' emotional responses to 14 audiovisual stimuli' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Affective Computing","Audiovisual stimuli","Edge computing","Fear","Machine Learning","Smart sensors","Virtual Reality"]} <|eot_id|>
 Here is the Description of a dataset. Description:'EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.Audio signals collected during laboratory experiment to measure participants' emotional responses to 14 audiovisual stimuli' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.Audio signals collected during laboratory experiment to measure participants' emotional responses to 14 audiovisual stimuli
Affective Computing; Audiovisual stimuli; Edge computing; Fear; Machine Learning; Smart sensors; Virtual Reality