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arxiv: 2504.00621 · v4 · submitted 2025-04-01 · 📡 eess.AS

Is ASMR Engineerable? A Signal Processing and User Experience Study

Zexin Fang , Bin Han , Henrik H. Sveen , C. Clark Cao , Hans D. Schotten This is my paper

Pith reviewed 2026-05-22 22:08 UTC · model grok-4.3

classification 📡 eess.AS
keywords ASMRacoustic designcyclic patternssignal processinguser studypredictabilityenergy density
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The pith

Smoothly spread energy-dense cyclic sound patterns trigger ASMR most effectively, making deliberate acoustic engineering feasible.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether ASMR can be produced on purpose by designing sounds instead of occurring by accident. It creates cyclic audio patterns that differ in predictability versus randomness, then measures how users respond in a controlled study. Signal features from the audio are extracted and linked through regression to the reported effects. If the link holds, ASMR becomes something that can be shaped at the signal level by choosing the right combination of smoothness and energy distribution rather than left to chance.

Core claim

The central claim is that ASMR responses can be systematically induced through controlled acoustic design. Cyclic sound patterns are constructed with varying degrees of predictability and randomness, then tested in a user study. Feature extraction from the signals followed by regression analysis produces an interpretable mapping. Results indicate that relaxing effects build progressively, do not depend on spatial orientation, and stay stable over time, with smoothly spread, energy-dense cyclic patterns proving most effective at triggering the response.

What carries the argument

Cyclic sound patterns varying in predictability and randomness, mapped to perceived ASMR via signal-processing feature extraction and regression analysis.

If this is right

  • Relaxing effects accumulate over repeated exposure and do not require specific spatial placement of the sound.
  • The response remains consistent across time once the pattern is set.
  • Tuning smoothness of spread and energy density in the cyclic structure directly improves trigger strength.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Custom audio tracks could be generated for targeted relaxation applications once the mapping is refined.
  • Combining the acoustic design with physiological recordings would test whether self-reports align with measurable bodily changes.
  • The same predictability-variation balance might apply to engineering other sensory or emotional audio responses.

Load-bearing premise

Self-reported effects from the structured user study accurately capture genuine ASMR responses produced by the acoustic parameters of predictability and variation.

What would settle it

A follow-up experiment that finds no reliable difference in reported ASMR between the designed cyclic patterns and unstructured noise would undermine the mapping.

Figures

Figures reproduced from arXiv: 2504.00621 by Bin Han, C. Clark Cao, Hans D. Schotten, Henrik H. Sveen, Zexin Fang.

Figure 1
Figure 1. Figure 1: Spectrograms of different audio clips DSS(α) = 1 N P f∈F DSS(f, α) and D˜ SS(α) = max f∈F DSS(f, α), where F represents the set of N frequency bins in DSS. On top of S(f), D˜ SS(α) and DSS(α) , we define eight features: X1 = Mean  D˜ SS , X2 = Var  D˜ SS , X3 = G  D˜ SS , X4 = Mean DSS , X5 = Var DSS , X6 = G [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: CCF of ASMR audio clips with different cyclic patterns [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: DSS(α) of ASMR audio clips with different cyclic patterns neutral, or stressful. Both effects were assessed using a 7- point scale. Although semantic contents were largely removed, which generally created confusion among participants, this does not indicate freedom from cognitive bias. We quanti￾tatively assessed this cognitive bias by asking participants to describe whether the clips seemed natural or art… view at source ↗
Figure 5
Figure 5. Figure 5: Average effect scores of different audio types [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Model optimization 1           1 [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Intercept of LMM where participants’ relaxation feeling builds up gradually. On the other hand, the results in Tab. I reveals a positive coefficient for X1 across all regression models, indicating that stronger cyclic patterns intensify observed effects, while the negative coefficient of X9 suggests that smoothly distributed frequency components contribute to triggering these effects. For the regression mo… view at source ↗
Figure 8
Figure 8. Figure 8: Intercept of LMM VI. CONCLUSION AND OUTLOOK In this paper, we have studied the relation between ASMR audios and human perception. The ASMR perception was studied in three aspects: the psychological effects, the phys￾ical effects, and the relaxing effect. Through surveys and regression, we have qualitatively verified that the perception to the relaxing effect is a cumulative process, while the spatial orien… view at source ↗
read the original 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

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper asks whether ASMR effects can be deliberately engineered at the signal level. It designs cyclic sound patterns whose predictability and randomness are controlled, runs a structured user study collecting self-reported relaxation and ASMR ratings, extracts acoustic features, and fits regressions to produce an interpretable mapping from those features to perceived effects. The central result is that smoothly spread, energy-dense cyclic patterns are most effective, supporting the claim that ASMR is engineerable.

Significance. If the regression mapping is statistically robust and the self-reports isolate genuine ASMR rather than nonspecific relaxation, the work would provide the first explicit signal-processing recipe for inducing ASMR and would open a route to reproducible, parameter-controlled ASMR content. The emphasis on interpretable acoustic features is a methodological strength that aligns with the journal's signal-processing focus.

major comments (2)
  1. [Abstract] Abstract: the reported regression results are presented without sample size, exclusion criteria, error bars, R² values, or any statistical test, so it is impossible to judge whether the claimed mapping between acoustic features and ASMR effects is supported by the data.
  2. [User-study description (likely §3)] User-study description (likely §3): the evaluation relies exclusively on self-reported effects; no concurrent EEG, skin conductance, or other physiological measures are mentioned, even though the abstract itself notes that prior ASMR validations use such bio-signals to distinguish true ASMR from general auditory relaxation. This choice is load-bearing for the engineerability claim.
minor comments (2)
  1. [Abstract] The abstract states that effects are “independent of spatial orientation” and “stable across time,” but the corresponding analysis or figure is not referenced, making it hard to locate the supporting evidence.
  2. [Methods / Feature extraction] Feature definitions and the exact regression model (predictors, response variables, regularization) should be stated explicitly in the methods section so that the mapping can be reproduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of statistical reporting and methodological scope. We address each major comment below and indicate the corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported regression results are presented without sample size, exclusion criteria, error bars, R² values, or any statistical test, so it is impossible to judge whether the claimed mapping between acoustic features and ASMR effects is supported by the data.

    Authors: We agree that the abstract should convey key statistical information to support assessment of the regression mapping. The full results section provides the sample size, exclusion criteria, R² values, error metrics, and statistical tests. We have revised the abstract to incorporate the sample size and R² values while preserving brevity, enabling readers to evaluate the claimed mapping without needing to consult the body text immediately. revision: yes

  2. Referee: [User-study description (likely §3)] User-study description (likely §3): the evaluation relies exclusively on self-reported effects; no concurrent EEG, skin conductance, or other physiological measures are mentioned, even though the abstract itself notes that prior ASMR validations use such bio-signals to distinguish true ASMR from general auditory relaxation. This choice is load-bearing for the engineerability claim.

    Authors: The study is explicitly framed as a user-experience investigation paired with signal-processing analysis, which permits systematic variation of acoustic parameters and direct regression onto self-reported ratings. While the abstract references prior bio-signal validations, the present contribution centers on establishing an interpretable acoustic-to-perception mapping via controlled cyclic patterns. We have added a dedicated limitations paragraph in the discussion that acknowledges the absence of concurrent physiological measures, clarifies that reported effects are self-rated ASMR-like sensations, and identifies physiological validation as a logical next step. This revision maintains the scope of the current work while addressing the concern about distinguishing ASMR from nonspecific relaxation. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical user study with new data collection

full rationale

The paper describes designing acoustic stimuli, collecting fresh user study responses on perceived ASMR effects, extracting signal features from those stimuli, and fitting a regression to map features to ratings. No equations, fitted parameters, or self-citations are shown to reduce the central claim (that certain cyclic patterns trigger ASMR) back to the inputs by construction. The derivation chain is the standard empirical pipeline of stimulus design → data collection → statistical mapping, which is self-contained against external benchmarks and does not invoke any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated.

pith-pipeline@v0.9.0 · 5717 in / 1071 out tokens · 38335 ms · 2026-05-22T22:08:41.906657+00:00 · methodology

discussion (0)

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Reference graph

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