Artificial ASMR: A Cyber-Psychological Approach

TL;DR

This paper explores how cyclic acoustic features in audio signals can trigger ASMR effects, using a cyber-psychological approach that combines AI, signal processing, and psychology to synthesize effective ASMR clips.

Contribution

It introduces a novel method to quantify and synthesize ASMR-triggering audio features without specific scenarios, advancing understanding of ASMR triggers.

Findings

Cyclic audio patterns can effectively trigger ASMR effects.

Synthesized ASMR clips without identifiable scenarios still induce ASMR.

A new interdisciplinary approach links acoustic features to psychological responses.

Abstract

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.