  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.
