Artificial ASMR: A Cyber-Psychological Approach
This addresses the need for scientific insights into ASMR triggers for researchers and content creators, though it appears incremental by building on known acoustic patterns.
The paper tackled the problem of understanding triggers for Autonomous Sensory Meridian Response (ASMR) by investigating the correlation between cyclic audio features and ASMR effectiveness, resulting in the synthesis of ASMR clips with random cyclic patterns that were proven effective in triggering ASMR effects.
The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR clips typically provide rich semantic information. With our attention caught by the common acoustic patterns in ASMR audios, we investigate the correlation between the cyclic features of audio signals and their effectiveness in triggering ASMR effects. A cyber-psychological approach that combines signal processing, artificial intelligence, and experimental psychology is taken, with which we are able to quantize ASMR-related acoustic features, and therewith synthesize ASMR clips with random cyclic patterns but not delivering identifiably scenarios to the audience, which were proven to be effective in triggering ASMR effects.