Is ASMR Engineerable? A Signal Processing and User Experience Study

[2504.00621] Is ASMR Engineerable? A Signal Processing and User Experience Study Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2504.00621 (eess) [Submitted on 1 Apr 2025 ( v1 ), last revised 9 Apr 2026 (this version, v4)] Title: Is ASMR Engineerable? A Signal Processing and User Experience Study Authors: Zexin Fang , Bin Han , Henrik H. Sveen , C. Clark Cao , Hans D. Schotten View a PDF of the paper titled Is ASMR Engineerable? A Signal Processing and User Experience Study, by Zexin Fang and 4 other authors View PDF HTML (experimental) Abstract: Autonomous Sensory Meridian Response (ASMR) has been remarkably popular in the recent decade, yet whether its effects can be deliberately engineered remains an open question. While ASMR effects validated through behavioral studies and neuro-physiological measurements such as electroencephalography (EEG) and related bio-signals, the acoustic mechanisms that trigger it remain poorly understood. We investigate whether ASMR responses can be systematically induced through controlled acoustic design, hypothesizing that cyclic patterns where predictability drives relaxation and variation sustains intrigue are key engineerable parameters. Specifically, we design cyclic sound patterns with varying predictability and randomness, and evaluate their effects via a structured user study. Signal processing-based feature extraction and regression analysis are used to establish an interpretable mapping between acoustic structure and perceived ASMR effects. Results show that relaxing effects accumulate progressively, are independent of spatial orientation, and remain stable across time. Crucially, smoothly spread, energy-dense cyclic patterns most effectively trigger ASMR, suggesting that signal-level engineering of ASMR experiences is achievable Comments: Submitted to IEEE Transactions on Human-Machine Systems Subjects: Audio and Speech Processing (eess.AS) Cite as: arXiv:2504.00621 [eess.AS] (or arXiv:2504.00621v4 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2504.00621 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bin Han [ view email ] [v1] Tue, 1 Apr 2025 10:18:39 UTC (1,230 KB) [v2] Wed, 2 Apr 2025 13:31:36 UTC (1,230 KB) [v3] Tue, 22 Apr 2025 20:43:11 UTC (1,541 KB) [v4] Thu, 9 Apr 2026 21:46:24 UTC (2,708 KB) Full-text links: Access Paper: View a PDF of the paper titled Is ASMR Engineerable? A Signal Processing and User Experience Study, by Zexin Fang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.AS < prev | next > new | recent | 2025-04 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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