EXPLORING SOUNDS THAT EVOKE AUTONOMOUS SENSORY

MERIDIAN RESPONSE

Nook Harquail

Dartmouth College

Hanover, NH

[email protected]

Michael Casey

Dartmouth College

Hanover, NH

[email protected]

ABSTRACT

Large Internet communities have formed around a genre

of sounds and videos known as ASMR. These sounds elicit

a physiological response described by subjects as ”pleasant

tingling” in the scalp and shoulders [4]. Despite the preva-

lence of ASMR videos and sounds on websites including

YouTube, SoundCloud, and Reddit, ASMR is virtually un-

studied by the scientiﬁc community. The term is notably

absent from academic literature, and as of March, 2015

there are no studies of brain activity in those who expe-

rience ASMR. Our aim is not to explore the neurological

basis of the claimed affect, but rather to shed light on the

sounds that produce the sensation. Using machine learn-

ing techniques, we explore the space of sounds that elicit

this physiological response. First, we create a corpus of

ASMR sounds using links from the /r/asmr subreddit on

Reddit.com. We extract audio features to summarize each

of the sound clips, and then use the Weka machine learn-

ing package to classify clips. We seek to answer a simple

question: what features separate ASMR sounds from other

sounds, including speech?

1. INTRODUCTION

A brief introduction to ASMR will help justify it as an

interesting subject. ASMR is a phenomenon in which a

stimulus (generally auditory) induces a tingling feeling in

subjects. Those who experience ASMR describe the sensa-

tion as ”a pleasurable, speciﬁc and intense tingling feeling

in the head and body upon hearing ”soft” or ”crackling”

sounds” [6]. This tingling is generally felt in the scalp (par-

ticularly, the occipital and parietal areas of the head), often

extending to the shoulders and spine. Those who experi-

ence ASMR have described the sensation as pleasurable,

relaxing and massage-like [4]. ASMR can refer either to

this sensation or the stimuli that evoke it.

The stimuli are even more varied than the sensation. A

2009 article on Vice.com describes ASMR videos:

 Nook Harquail, Michael Casey.

Licensed under a Creative Commons Attribution 4.0 International Li-

cense (CC BY 4.0). Attribution: Nook Harquail, Michael Casey. “Ex-

ploring Sounds that evoke Autonomous Sensory Meridian Response”,

16th International Society for Music Information Retrieval Conference,

2015.

Figure 1. Frame from an ASMR video featuring bub-

ble wrap sounds. ASMR videos are feature close, quiet

sounds: although the performer manipulated the bubble

wrap for over 20 minutes, no bubbles popped [7].

...pretty young women talking softly and pre-

tending to be travel agents; a pair of hands

stroking and crinkling plastic bags in almost

disturbingly sensual ways; another pair of hands

opening a box of Legos; a 12-minute long pretend-

eye exam monologue with no video [5].

The most common triggers include whispering, crisp

sounds (such as crinkling paper), tapping, and close per-

sonal attention [4]. Although little is know for certain, the

wide range of ASMR stimuli and anecdotal evidence from

ASMR community members suggests that there may be no

universal stimulus that will trigger ASMR in all subjects

who experience ASMR.

Perhaps most striking is the strength of online ASMR

communities. Synaesthesia, which is a relatively well-studied

phenomenon, has fewer than 5000 members of online com-

munities across Reddit and Facebook, while ASMR com-

munities top 100,000 members (See Table 1 on page 2).

Wilson and Peterson deﬁne online communities as having

two principles components: Access and Identity [9]. In

the case of ASMR, the community is only truly accessible

to those who experience the phenomenon. Certainly, those

who do not experience ASMR can access the same content,

but they will likely quickly leave the community, because

the videos are relatively uninteresting, and comments cen-

ter around which sections of the video trigger tingling for

Community Size

GentleWhispering on YouTube 385k viewers

ASMRrequests on Youtube 218k viewers

EphemeralRift on Youtube 111k viewers

/r/asmr on Reddit 98k subscribers

ASMRofﬁcial on Facebook 24k likes

ASMRSounds on SoundCloud 261 listeners

Society of Sensationalists on Yahoo 168 members

Table 1. Sizes of various ASMR communities, including

the top three ASMR YouTube channels by number of sub-

scribers.

individual. ASMR Identity is deﬁned along similar lines.

Because of the inherent inaccessibility of ASMR, ASMR

community members often feel outside the mainstream ex-

perience, even as if they are engaging in ”transgressive in-

timacy” [1]. This is especially true of creators of ASMR

content, who typically do not reveal their real names, and

often refuse to give interviews [2]. Since ASMR videos

are widely watched on YouTube, ASMR content creators

can earn signiﬁcant income if their videos contain adver-

tisements.

Although online communities for ASMR have existed

since at least 2007, the term ASMR was coined in 2009 as

the name of an online community on Facebook [2]. The

term — Autononmous Sensory Meridian Response — de-

liberately evokes scientiﬁc jargon, indicating a desire for

legitimacy and scientiﬁc recognition: ”the ASMR commu-

nity has tried to ground their discussions of the experience

in scientiﬁc terms that suggest empirical proof of its ex-

istence” [2]. An earlier community referred to the expe-

rience as Attention Induced Head Orgasms (AIHO), but

ASMR is now the dominant term.

Due to the strength of ASMR communities, ASMR re-

search (albeit unscientiﬁc), usually centers around the on-

line communities. Since the sensation appears to be rel-

atively rare, and the community is eager to engage with

researchers, online ASMR communities are a good way

to gain subjects for studies and surveys: ”participants pre-

sented themselves as volunteers via online 97 advertise-

ment on specialised ASMR interest groups on Facebook

and Reddit.”

ASMR videos are relatively long: while the average

video length on Reddit is just under two minutes, the aver-

age length of ASMR videos in our sample was 11 minutes.

It is not uncommon for ASMR videos to be over 40 min-

utes long.

Although the triggers for ASMR generally contain a vi-

sual component, we justify focusing on sounds for two rea-

sons:

• Audio-only ASMR communities exist

, but we have

not found any communities that primarily focus on

visual stimuli.

For example, the ASMR Audio group on SoundCloud:

https://soundcloud.com/groups/asmr-audio.

• ASMR videos are more acoustically similar than vi-

sually similar, so audio provides a better . . .

The ASMR Reddit community rejects gifs and silent

videos, apparently not considering them to be legitimate

triggers.

Listening to ASMR sounds is typically a solitary activ-

ity. The ASMR subreddit sidebar suggests listening with

headphones (many ASMR videos feature binaural sounds)

in a dark or dimly-lit environment. As ASMR is by na-

ture a personal experience, it is perhaps unsurprising that

very speciﬁc communities cater to small subsets of ASMR

stimuli. One community excludes whispering and talk-

ing, another only includes monologues; one excludes male

performers, another excludes performance itself — requir-

ing that the ASMR be produced ”unintentionally.” Ahuja

posits ASMR as an antidote to ”isolation mediated by moder-

nity”. He hypothesizes that ASMR is the result of ”a kind

of hypersensitivity to touch in the setting of its relative de-

ﬁciency” [1]. The ”touch” in the case of ASMR, is pri-

marily auditory. The sound qualities create an illusion of

closeness reminiscent of physical proximity.

We might also deﬁne what ASMR is not. ASMR is not

traditionally musical: the sounds are not organized by pitch

or any regular rhythm. The ASMR community on Reddit

deﬁnes frisson as a related phenomenon, in which music

elicits shivers or tingling. However, Reddit users orga-

nize frisson and ASMR as different sensations, evidenced

by the existence of /r/asmrmusic as separate from /r/asmr

(with no content shared between them). Nor is ASMR

purely random: white noise was not found to be a strong

trigger in a study with 475 participants who self-reported

experiencing ASMR [4].

ASMR is sometimes compared to other types of plea-

sure: drug induced euphoria, and particularly orgasm. The

emphasis on role play in ASMR videos makes it easy to

draw comparisons to sexual fetishes, but the video sub-

jects are not uniform enough to be a true fetish. Rather

than focusing on a speciﬁc situation or physical attribute,

ASMR role play videos emphasize situations that involve

personal attention, including everything from hair cutting

to a whispered conversation [1]. Community members in-

sist that the phenomenon is asexual, and object to compar-

isons between ASMR and sexual arousal. A typical com-

ment reads:

As I’ve stated before, I really don’t like the

sexualization of ASMR. I think it is very detri-

mental to the ”community” to have sexual terms

in the description of this subreddit. The ﬁrst

example I can think of is an elementary teacher

I know that gets ASMR from certain speech

patterns like most of us. One of the teacher’s

students gives her ASMR. I really don’t think

people who do not understand ASMR would

ﬁnd it kosher if they thought the teacher was

having ”head orgasms” at school from one of

her very young students, when really it’s just

tag occurrence per 1000 posts

intentional 286

female 256

unintentional 233

male 213

soft spoken 146

asmr 116

whispering 87

binaural 81

tapping 77

whisper 72

soft-spoken 57

british accent 43

Table 2. Most common tags for videos in the Reddit

ASMR community.

Figure 2. Tags appear in brackets.

a relaxing speech pattern.

The main ASMR subreddit does not allow sexual con-

tent, pushing it to a small splinter community (”Not Safe

for Work ASMR) for erotic content that induces ASMR.

The relative size of these communities belies claims that

ASMR is primarily erotic.

ASMR is also often compared with synaesthesia, al-

though the two present quite different somatic responses

[3]. The phenomena may be related, as those who report

experiencing ASMR are more likely to report experiencing

synaesthesia than the general population [4].

2. METHODOLOGY

We wrote a script using Node.js to extract audio posted

by the ASMR community on Reddit. The script identiﬁes

posts from http://reddit.com/r/asmr that link to videos on

YouTube. We then converted the ﬁles to Waveform Audio

File Format with a uniform sampling rate of 22.05 kHz.

The Reddit ASMR community uses a tagging system to

allow users to ﬁnd content with speciﬁc triggers (See ﬁgure

2 on page 3). While downloading videos, we collected tags

to compare the triggers with an existing study on ASMR

triggers. We wrote a short script to organize audio ﬁles

Comment on the ASMR subreddit by user elizabethmeghan.

http://www.reddit.com/r/asmr/comments/1dagb3/c9ojbj0

into a folder based on their tag, and attempted to classify

ASMR samples based on their tags using Weka.

To reduce the effect of video length on our results, we

only extracted features from the ﬁrst ﬁve minutes of each

audio ﬁle. We did not normalize the data, because normal-

izing sounds reduced the accuracy of Weka”s classiﬁca-

tion. A notable feature of ASMR videos is their relatively

low amplitude, so it is unsurprising that non-normalized

audio yielded better results.

Some ASMR videos include a musical introduction, so

we excluded the ﬁrst 30 seconds from the ASMR sam-

ples to reduce the effect that introductions had on classi-

ﬁcation. Removing introductions improved the accuracy

of the Weka classiﬁer (a Multilayer Perceptron with 20%

training) by 0.5%. As relatively few non-ASMR videos

contained introductions, we extracted features from the full

duration of the non-ASMR clips. We also removed 43 du-

plicate samples, by searching for ﬁles with similar lengths

(within 1 second) and manually identifying duplicates. Du-

plicates occur when the same video is uploaded by a dif-

ferent user and then submitted again to Reddit. We ex-

clude posts with the [META] tag, as they include con-

tent about the phenomenon and community. Videos linked

from [META] posts do not necessarily share characteristics

with sounds that evoke the ASMR response.

Thus, we constructed a corpus of ASMR sounds. For

comparison, we needed a representative sample of non-

ASMR sounds. We collected non-ASMR sounds using the

same method used to created the ASMR corpus. In addi-

tion to non-ASMR sounds from the /r/videos subreddit, we

collected sounds from the /r/speeches subreddit as part of

the non-ASMR sample. The inclusion of these sounds en-

sures that we classify ASMR sounds a separate from nor-

mal speech.

We extracted audio features using the Marsyas (Music

Analysis, Retrieval and Synthesis for Audio Signals) pack-

age, exporting to an .arff ﬁle. We used bbextract from

Marsyas to extract timbral features as well as stereo pan-

ning spectrum features. We included stereo panning spec-

trum features, because ASMR performers often use binau-

ral microphones to create . Including stereo features im-

proved the accuracy of the Weka classiﬁer (a Multilayer

Perceptron with 20% training) by 0.8%.

Finally, we imported the extracted features into Weka

for analysis. Weka provides tools for visualizing and clas-

sifying data using machine learning and clustering. We

used unsupervised classiﬁcation (K-means clustering) and

supervised classiﬁcation (Naive Bayes and a Multilayer

Perceptron). For the supervised classiﬁers, we trained with

both a 5% and 20% split between training and test data. We

also plotted the samples along their principle components.

Due to copyright concerns, we cannot share the audio

ﬁles themselves, but the code used to extract audio from

Youtube videos is freely available on Github

. The ex-

tracted features and miscellaneous functions used in the

analysis can also be found on github

http://github.com/harquail/subredditTomp3s

https://github.com/harquail/asmrProjectHelpers

Figure 3. Samples plotted along their ﬁrst two principle

components. ASMR samples appear in red, non-ASMR

samples in blue.

Figure 4. Samples plotted along their ﬁrst two principle

components. Samples tagged as [unintentional] appear in

red, samples tagged as [intentional] in blue.

3. RESULTS AND DISCUSSION

Classifying ASMR and non-ASMR sounds revealed that

ASMR sounds have unique sound qualities that separate

them from non-ASMR sounds. We extracted features from

1002 samples of ASMR sounds, and 1000 samples of non-

ASMR sounds. Even without training data (only the de-

sired number of groups was given), a naive clustering al-

gorithm was able to achieve 77.5% success in classifying

sounds based on their features. With training data and a

more sophisticated learning algorithm, we were able to

achieve 87.8% success (See Table 3 on page 4). We can

speculate that these qualities include relatively now vol-

ume, a high amount of noise, and sounds than pan across

channels.

Manually examining outliers yields even greater conﬁ-

dence in the classiﬁcation. For example, one ASMR video

that Weka classiﬁed as non-ASMR was also controversial

on Reddit, with community members complaining that the

sounds were too jarring to induce ASMR

. Non-ASMR

sounds classiﬁed as ASMR tended to be relatively quiet,

or contain a lot of noise

The video in question featured a manual typewriter:

http://www.youtube.com/watch?v=c S5Ttgbw

For example, Bane”s monologue from ”The Dark Night Rises” was

likely classiﬁed as ASMR due to sounds of quiet voices, rain, and mufﬂed

Algorithm Accuracy

Multilayer Perceptron, 20% training 87.8% correct

Multilayer Perceptron, 5% training 83.3% correct

Naive Bayes, 20% training 80.2% correct

Naive Bayes, 5% training 79.4% correct

Unsupervised k-means clustering 77.5% correct

Table 3. Accuracy of classiﬁcation methods in classifying

ASMR and non-ASMR audio ﬁles.

Algorithm Accuracy

Multilayer Perceptron, 20% training 70.4% correct

Multilayer Perceptron, 5% training 62.9% correct

Naive Bayes, 20% training 54.8% correct

Naive Bayes, 5% training 54.3% correct

Unsupervised k-means clustering 49% correct

Table 4. Accuracy of classiﬁcation methods in classify-

ing ASMR audio ﬁles tagged as [intentional] and [uninten-

tional].

In general, we observed less variation in ASMR sounds

than in non-ASMR samples (see Figure 3 and Figure 4 on

page 4). This is consistent with the notion that ASMR

sounds are a speciﬁc subset of a wide range of sounds, and

that is is possible that some sounds in our non-ASMR sam-

ple would be considered ASMR by those who experience

it.

We also ran the classiﬁcation on ambiguous data: sounds

from the /r/poetryreading subreddit. These sounds have

many similarities to ASMR, but are often too jarring to

be relaxing. Anecdotally, the poetry readings classiﬁed as

ASMR tended to be quieter and have lower sound quality

than those classiﬁed as non-ASMR.

We also attempted to classify sounds with the ASMR

corpus, training based on the [unintentional] and [inten-

tional] tags. We ran the same classiﬁcation algorithms

on 329 sounds tagged as intentionally created to induce

ASMR, and 224 sounds tagged as accidentally inducing

ASMR. This classiﬁcation was relatively unsuccessful, yield-

ing a maximum success rate of 70.4%, with unsupervised

clustering performing equivalently to random guessing (See

Table 3 on page 4). This result seems to indicate that

ASMR stimuli have similar qualities, regardless of whether

not they are intentionally produced. We did not have enough

data to run meaningful tests on any other tags.

4. FUTURE WORK

As a relatively unstudied phenomenon, ASMR provides

rich opportunities for research. There is an obvious need

for fMRI studies to examine brain activity in those who

experience ASMR compared to those who do not. Be-

cause of participants” claims that ASMR is relaxing and

helps induce sleep, some hope that ASMR may be use-

explosions: https://www.youtube.com/watch?v=bpmNgPzklmQ.

ful in treating post-traumatic stress disorder and in treating

sleep disorders [8].

There are also many possible extensions of our work.

We describe a pipeline that makes it easy to extract fea-

tures from an online community that posts and tags sound

clips. This could be easily transferred to examine other au-

dio communities, or extended to examine the ASMR com-

munity in more detail. For example, it would be trivial

to collect text from comments while extracting videos and

analyze comments associated with ASMR videos or their

audio features.

Another interesting extension would be to use similar

learning techniques to develop an algorithm for generating

sounds that widely induce ASMR. This would be usfeul

for future human-subject studies on ASMR (and valuable

to the ASMR community).

5. REFERENCES

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vision & New Media, page 1527476414556184, 2014.

[3] Simon Ed Baron-Cohen and John E Harrison. Synaes-

thesia: Classic and contemporary readings. Blackwell

Publishing, 1997.

[4] Emma L Barratt and Nick J Davis. Autonomous sen-

sory meridian response (asmr): A ﬂow-like mental

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[5] Harry Cheadle. Asmr, the good feeling no one can ex-

plain, July 2012.

[6] Olympia Colizoli, Jaap MJ Murre, and Romke Rouw.

A taste for words and sounds: a case of lexical-

gustatory and sound-gustatory synesthesia. Frontiers in

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[7] DonnaASMR. Binaural asmr. bubble wrap: Asmr-

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[8] Sarah Jones. April 30 2014 a scientiﬁc and theoretical

study on frisson and autonomous sensory meridian re-

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[9] Samuel M Wilson and Leighton C Peterson. The an-

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