ASMRcade: Interactive Audio Triggers for an Autonomous Sensory Meridian Response

Autonomous Sensory Meridian Response (ASMR) is a sensory phenomenon involving pleasurable tingling sensations in response to stimuli such as whispering, tapping, and hair brushing. It is increasingly used to promote health and well-being, help with sleep, and reduce stress and anxiety. ASMR triggers are both highly individual and of great variety. Consequently, finding or identifying suitable ASMR content, e.g., by searching online platforms, can take time and effort. This work addresses this challenge by introducing a novel interactive approach for users to generate personalized ASMR sounds. The presented system utilizes a generative adversarial network (GAN) for sound generation and a graphical user interface (GUI) for user control. Our system allows users to create and manipulate audio samples by interacting with a visual representation of the GAN’s latent input vector. Further, we present the results of a first user study which indicates that our approach is suitable for triggering ASMR experiences.


INTRODUCTION
In today's fast-paced world, stress is increasingly becoming a pervasive and challenging problem in people's daily lives [44,48].Persistent stress can lead to a variety of physical and mental health issues, such as anxiety, depression, sleep disorders, heart disease, high blood pressure, headaches, and many other health problems [13,16,19,25,32,45].Therefore, it is very important for people to fnd an adequate balance to relax and thus counteract stress.
An efective method of relaxation that is becoming more and more popular is the consumption of content triggering an Autonomous Sensory Meridian Response (ASMR) [1].ASMR is a term used to explain the tingling sensation some people experience in response to certain, mostly auditive, stimuli.Typically, this feeling is described as a delightful buzz that begins in the scalp and travels down the neck and back [2].Auditive triggers for ASMR can vary, including sounds such as whispering, soft speaking, tapping, or scratching.Additionally, visual or physical stimuli, like observing someone do a repetitive task or receiving a gentle touch, can also elicit ASMR.In recent years, ASMR has become increasingly popular, with numerous content creators producing videos and audio recordings intended to induce ASMR in their audience.While there is limited scientifc research on the topic, many individuals fnd ASMR to be a relaxing and pleasurable experience [1].However, it's important to note that not everyone experiences ASMR or is afected by sounds meant to induce ASMR in the same way.
ASMR is often considered a passive experience because it involves a state of relaxation and receptivity to certain stimuli rather than an active engagement with them.However, Barret et al. [1] found a correlation between the number of ASMR responses and the fow state of participants, which is the psychological state of being immersed in a task [5].Hence, as such tasks typically involve active participation or interaction with the environment, e.g., in sports or playing video games, we propose an active approach for ASMR triggers that interactively engages ASMR recipients.
To this end, this paper introduces ASMRcade, an interactive application for ASMR sound synthesis, exploration, and customization.The application utilizes a Generative Adversarial Network (GAN) that we trained to generate high-quality audio samples that are aimed to trigger ASMR.By providing a graphical user interface, users of ASMRcade are able to interact with that model's latent space and, as such, directly infuence the sounds that the GAN produces in real time.
The main contributions of this paper are as follows: • We trained a WaveGAN [8] model on a dataset of ASMR sounds.Specifcally, for a proof-of-concept, we use tapping sounds, which have proven to induce ASMR for many people in the past [2].Such sounds are typically created by tapping on various objects, such as surfaces, or directly on a microphone.
• Using a web-based graphical user interface as introduced by Schlagowski et al. [37], we made the input latent space of the trained WaveGAN model interactive.Specifcally, we represent parts of the latent model space of the WaveGAN through certain interface elements.By moving those elements, the user directly infuences the sounds that the model produces.
• We present a frst exploratory user study in which we let users interact with our system.We show that our system can induce ASMR and, as such, is a promising direction for future research in the feld of ASMR.
To the best of the authors' knowledge, this paper is the frst work introducing an interactive approach to the feld of ASMR research.The online demonstrator using this GUI and the GAN described above can be found online on our web server for demonstration purposes 1 .

RELATED WORK 2.1 Autonomous Sensory Meridian Response
Autonomous Sensory Meridian Response (ASMR) is a sensory phenomenon characterized by a pleasurable tingling sensation with its origin in the scalp, moving down the neck, and sometimes following the line of the spine down to other areas of the body in response to specifc audio and visual stimuli.In recent years, there has been a growing interest in understanding the triggers that lead to ASMR experiences and the potential benefts of experiencing ASMR.As such, the research community has begun to embrace the potential of ASMR as a valuable source of inspiration for Human-Computer Interaction [20].Barratt et al. [1] proposed that ASMR is a fow-like mental state, which refers to a state of intense focus and absorption in an activity.They suggested that ASMR experiences involve a deep immersion in the sensory stimuli, which can lead to a sense of timelessness and an altered state of consciousness.The researchers also found that people who experience ASMR report a range of positive efects, such as relaxation, stress relief, and improved mood.
In their later study, Baratt et al. [2] investigated the sensory determinants of ASMR by surveying a large online community of individuals who experience ASMR.They found that the most common triggers were related to auditory stimuli, such as whispering, tapping, and scratching sounds.Other common triggers included visual stimuli, such as hand movements and personal attention, and touch-related stimuli, such as gentle touch and hair brushing.Interestingly, the researchers also found that individual factors, 1 www.hcai.eu/asmrcadesuch as personality traits and mood, could infuence the likelihood of experiencing ASMR.
Poerio et al. [33] further explored the triggers of ASMR by conducting laboratory experiments in which participants listened to a range of audio and visual stimuli while their physiological responses were measured.They found that ASMR experiences were associated with a reduction in heart rate and an increase in skin conductance responses, indicating a relaxation response.The study also found that the most efective ASMR triggers were those that involved interpersonal closeness, such as whispering and soft-spoken voices.
In terms of content, people consume a variety of ASMR videos and audio recordings to experience the tingling sensation.Common types of content include role-playing scenarios, where the so-called ASMRtist portrays a particular role (such as a hairdresser or a doctor), soundscapes, where various sounds are played, and guided meditations or afrmations.There are many successful "ASMRtists" with millions of subscribers on YouTube, indicating the existence of a vast online community interested in the phenomenon.
Overall, ASMR is a complex phenomenon that involves a combination of sensory and personal factors, and triggers can vary widely between individuals.However, research has shown that certain types of stimuli, such as gentle sounds and interpersonal closeness, are more likely to elicit an ASMR response.Furthermore, the fow-like mental state experienced during ASMR has been proposed to have potential benefts for mental health and well-being.

Customizable Sound Generation
The technical foundation to make an interactive ASMR experience possible is a method to synthesize artifcial sounds in a controlled way.While early works in that feld made use of procedural approaches like Concatenative Sound Synthesis [40] or Modular Sound Synthesis [46], mechanisms using machine learning are on the rise since several years.For example, approaches from the feld of Evolutionary Computing are widely used for optimizing sound synthesis problems [22,27,28] and were, although not in an interactive setting, also applied to ASMR sound synthesis [30].One promising approach for sound generation is the use of Generative Adversarial Networks (GANs), which were originally developed for image synthesis [14].Various modifcations to the original GAN architecture were presented that address the generation of audio data, such as GANSynth [11] or WaveGAN [9].
In order to add the possibility for controlling the audio outputs generated by a GAN, mechanisms were proposed that incorporate directive features into the GAN training [10,24], or directly search through the input space of an already trained GAN, e.g., using methods such as Latent Variable Evolution (LVE) [26].In the context of ASMR, Fang et al. [12] adapted the DCGAN architecture [34] to create random new ASMR sounds.A similar approach was followed by Oh et al. [31], who created artifcial ASMR sounds using the SpecVQGAN architecture [17].However, although evaluations of both of these approaches indicated that GANs could create auditively pleasing ASMR sounds, they did not include mechanisms to engage with the sound synthesis process interactively.

Interactive Approaches to Parameter Space Exploration
The generation of ASMR audio content is only one part of our objective -another goal is to actively engage users in the ASMR sound generation process.For the ASMRcade application, we use the WaveGAN architecture for sound synthesis, which can transform an arbitrary, non-interpretable input vector to sound that resembles the data the model was trained on.Therefore, we have to enable the user to interact with the input parameter space of the WaveGAN.In the general feld of audio synthesis, multiple approaches exist to interactively explore parameter spaces, such as mapping parameters to 2D-Interfaces using Hilbert curves [47], or using interactive evolutionary algorithms [6,36].In the context of music synthesis, interaction with parameter spaces was even used as musical instruments [3,18,29,42].Further, several works have focused on building interactive systems specifcally for exploring a GAN's input parameter space.Here, a popular use case is to interactively control the output of GANs that were trained to generate drum sounds.For instance, Ramires et al. [35] used SeFa, a closed-form factorization method [41], to fnd dimensionality-reduced directions of a GAN's parameter space, which they made accessible to the user through a Graphical User Interface (GUI).Further, Schlagowski et al. [37,39] aimed for interactive drum sound synthesis for a drum sequencer application.Similar to our work, they used the WaveGAN architecture and built a user interface to directly modify single latent vector elements of a GAN [37,39].As they observed positive user experience ratings in their user study, we decided to include certain GUI-elements of their system within our system.

THE ASMRCADE SYSTEM
Our system consists of two major parts.Firstly, a WaveGAN model that was trained to produce highly realistic and diverse ASMR tapping sounds, and secondly, a web-based graphical user interface, which includes visual representations of the WaveGAN's latent input vector elements.By interacting with those visual representations, i.e., changing their spatial position, the user can directly infuence the WaveGAN's audio output in real-time.All in all, by using our system, the user can actively engage in the ASMR sound generation process instead of just passively listening to ASMR stimuli.The following sections give a more detailed overview of the ASMRcade system's single components.

Audio Generation
In recent years, Generative Adversarial Networks (GANs) have excelled in creating high-quality, diverse data that is nearly indistinguishable from data that was used for training ("real" data).GANs were frst introduced by Goodfellow et al. [15] and consist of two neural networks -a generator and a discriminator -which compete against each other in a min-max game.During training, the generator learns to convert random noise vectors to new, artifcial data, while the discriminator learns to distinguish between that data and real data from a training dataset.As such, the generator tries to fool the discriminator while the discriminator tries not to be fooled by the generator.A modifcation of the original GAN framework was introduced by Donahue et al. [8], who presented the WaveGAN architecture.While originally, GAN models were used for image synthesis, WaveGAN was specifcally designed for audio synthesis.We selected the WaveGAN architecture for our system as it is fast and fulflls the required quality standards of the generated output for successful ASMR triggers.

Dataset.
A necessary prerequisite for training a WaveGAN model is the existence of a suitable dataset.Datasets that can be used to train a GAN for high-quality content generation must be restricted to short, non-verbal, and quality-consistent data [49].As existing ASMR datasets did not fulfll these requirements, we collected data by processing suitable ASMR YouTube videos.For a proof-of-concept, we focused on tapping sounds, which were found to be a well-functioning ASMR trigger for many people [2].We used tapping-only videos of content creators well-known to the ASMR community, namely "ASMR Bakery"2 , "LottieLoves ASMR"3 , "Coromo Sara" 4 and "ricarda" 5 .We downsampled the audio to 16kHz mono using FFMPEG to ease the GAN's learning task.One limitation of GANs is that they require a fxed output length defnition.Therefore, we extracted 2,000 audio segments of 1 second length using a threshold-based segmentation.Further, we applied Fade-In and Fade-Out efects to the segments.By doing so, we got rid of unwanted cracking or popping sounds at the start or end of the audio sample that the generator might otherwise reproduce.We divided the dataset into training, testing, and validation sets (70% train, 20% val, 10% test) to ensure robust evaluation and validation of the GAN model.

GAN
Training.We used the WaveGAN architecture presented by Donahue et al. [8].WaveGAN is a modifcation of the DCGAN [34] architecture, which in turn extends the original GAN framework by replacing the fully connected layers by convolutional and deconvolutional layers.WaveGAN modifes the DCGAN to work well specifcally with audio data, e.g., by replacing 2D kernels with 1D kernels and other adapatations.For a full architecture description, please refer to the WaveGAN publication [8].The model was trained from scratch using the exact training confguration and hyperparameters recommended by Donahue et al. [8].After 20k training steps, the generated audio samples resembled tapping sounds with various surfaces like glass and wood, and no further improvement could be observed.Minor artifacts were still present, but the overall results were satisfactory.

User Interface
Schlagowski et al. [37] compared diferent graphical user interfaces, called Vector Manipulation Modules (VMMs), that make audio generated by WaveGAN customizable.Opting for a VMM design with high hedonistic UEQ ratings in Attractiveness, Stimulation, and Novelty, we adopted one of their designs representing elements of the latent input space in 3D spheres (see Fig. 1).The user can drag and drop these spheres to adjust their corresponding numerical value in the latent input space of the GAN and hear the resulting change of the generated audio sample in real-time.As the Wave-GAN model that we trained transforms a 100-dimensional noise input vector to audio data resembling ASMR tapping sounds, there are 100 manipulatable spheres in the GUI.The numerical latent space value is proportional to the spatial distance of the dragged (smaller) sphere to an inner and outer sphere, representing the minimum and maximum thresholds of the corresponding latent space value (semitransparent spheres in Fig. 1).The initial 3D positions are calculated using the fbonacci lattice algorithm [43].
Other application features include controls to add a Reverb efect, control the volume, and completely reshufe the current input vector.Further, an additional Size slider is part of the system.By moving that slider, the number of controls for the input vector can be decreased so that the user can focus on only a few dimensions instead of all 100.
We embedded both the VMM and a Javascript-based version of WaveGAN into a webpage that uses the browser to display the VMM, run the GAN, and playback generated audio.Additional features of the resulting demonstrator system include: (1) Playback of the last generated ASMR sound in a loop. (2)An optional "Hands of" mode, in which the input vector automatically changes each time the sound is played within the loop. (3)A tutorial mode, which explains the system by pointing to certain AI elements and describing their features.

EVALUATION
To evaluate if our application is suitable to induce an ASMR experience, we conducted an explorative user study in which participants had to interact with the ASMRcade system.Therefore, we made ASMRcade available on a web server and invited online participants to test it.

Study Group
We wanted to ensure that a good portion of our participants were already familiar with and actively engaging with the concept of ASMR.Thus, we targeted the existing ASMR community by creating a post in the subreddit "r/ASMR", an online platform where people share ASMR-related experiences and information on a regular basis.There, we briefy explained the application and invited users to test the ASMRcade application and complete the associated questionnaires.As a result, we acquired 17 Participants, which primarily consisted of users from the "r/ASMR" subreddit and others who discovered the Reddit post.

Study Procedure
During the study, the participants could frst interact with the ASMRcade system for as long as they liked.After that, they had to complete an online questionnaire which consisted of three diferent parts: (1) Personal information: This part contained three questions regarding age, gender, and familiarity with ASMR to understand the demographic profle of the participants. (2)User experience questionnaire: We used the user experience questionnaire (UEQ) [23] to assess how participants experienced the interaction with the system.The questionnaire contains 26 items measuring six dimensions of user experience: Attractiveness, Perspicuity, Efciency, Dependability, Stimulation, and Novelty.Each item is represented by two opposing terms and a corresponding seven-point scale ranging from -3 (most negative) to 3 (most positive), with 0 indicating a neutral response.To maintain the integrity of the online questionnaire, we followed the UEQ Handbook's advice to remove suspicious data.As such, participant responses were considered suspicious if: • They gave vastly diferent answers for questions within the same scale, and this occurred in at least three scales.• They provided identical answers to more than 15 of the 26 questions. (3)ASMR-specifc questions: The fnal section consisted of questions related to the participants' experience of ASMR while using ASMRcade.These questions aimed to gather information about whether participants experience ASMR in general, whether they experienced it while using ASMRcade, factors infuencing the triggering or lack thereof, the time spent with the ASMRcade application, suggestions for improvement, likes and dislikes, opinions on the generated sounds and the interaction with the system.Specifcally, we asked the following questions: • Q1: Do you experience ASMR in general?(Binary choice) • Q2: Have you experienced ASMR while using the ASMRcade?(Binary choice).Depending on their answer, participants had to provide free-form feedback on how they interacted with the application when ASMR was triggered (Q2.In the following sections, we present the results of the online study.A total of 17 participants fully completed the survey.However, two participants reported spending 0 minutes with ASMRcade, leading to their removal before data analysis.• Gender: The sample included 10 males, 4 females, and 1 participant who preferred not to answer.

• Familiarity with ASMR:
-1 participant never heard about it before -2 participants heard about it but never consumed ASMR content -11 participant consumed ASMR content before -1 participant consumed ASMR content occasionally -0 participants consumed ASMR content regularly 4.3.2User Experience Qestionnaire (UEQ).To maintain the integrity of the questionnaire, we removed suspicious data as suggested in the UEQ Handbook's guidelines.As such, two data entries were removed, leaving 13 valid samples for analysis.We used the analysis tool provided by Laugwitz et al. [23] to analyze participants' responses.The results for the UEQ scales are presented in Table 1 and depicted in Figure 2.
The best-performing scale was Novelty (mean score of 1.712), followed by Attractiveness (1.551) and Perspicuity (1.5).Efciency and Stimulation scored 1.25 and 1.365, respectively.The lowestperforming measure was Dependability, with a mean score of 0.904.However, according to the UEQ handbook, mean scores above 0.8 represent a good evaluation.Our application surpasses that value across all dimensions.

Scale
Mean

ASMR related questions.
In the following, we present the results of the ASMR-specifc questions from the online questionnaire.These questions aimed to assess participants' general ASMR experiences and their experiences with ASMRcade.• General ASMR Experience: 8 participants reported that they generally experience ASMR.• ASMR Experience in ASMRcade: 7 participants indicated that they had experienced ASMR while interacting with ASMRcade.• ASMR Experience Overlap: Among the 8 participants who reported general ASMR experiences, fve also experienced ASMR while using ASMRcade.ASMRcade triggered an ASMR experience for two users that generally don't experience ASMR.• Time Spent in ASMRcade: On average, participants spent 12.47 minutes interacting with ASMRcade.The minimum reported time was 3 minutes, and the maximum was 30 minutes.
To analyze the free-form feedback from participants, an inductive thematic analysis [4] was conducted, which helps identify and report patterns within textual data.This qualitative method allows for understanding participants' experiences and opinions without imposing predetermined categories or theoretical perspectives and was previously used in similar studies (e.g., [7,21,38,50]).
Code/word clouds were created using the themes and codes identifed in the analysis to represent user feedback visually.Code clouds display words or phrases in varying font sizes, with larger sizes representing higher occurrence frequency within the data.The feedback by users that had experienced ASMR (green) includes aspects such as intuitive interaction (1 mention; e.g., "Changing the sounds became way more intuitive"), reshufing (2 mentions; e.g., "Reshufing until I experienced it"), associating sounds with daily life (1 mention; e.g., "My brain started to fnd references from my daily life from which the sound could be known and tried to fnd   rhythm and movement that could be applied to the sound efects"), and using headphones (1 mention).
The feedback on what was missing to experience ASMR (red) encompasses comments on the need for longer sound clips (1 mention), diferent sounds (3 mentions; e.g., "Other sounds" and "Diferent sounds to choose from, not just tapping"), more natural sounds (1 mention; e.g., "I would like the sounds to be more natural, they felt kind of mechanical in the ASMRcade"), and smoother transition between loops (1 mention; e.g., "The transition between the loops was pretty noticeable which made it feel less natural to me").
Figure 7 presents the code cloud generated based on participants' feedback on the generated sounds in ASMRcade (i.e., Q7).The positive feedback (green) includes aspects such as sound quality (4 mentions; e.g., "The sounds were pleasant" and "The tapping sounded convincing"), sound realism (2 mentions; e.g., "The knocking sounds were pleasant"), good sound variety (1 mention), reverb efect (1 mention) and unique experience (1 mention; e.g., "The loops added a unique and enjoyable aspect to the experience").
On the other hand, negative feedback (red) encompasses comments on the need for greater sound variety (3 mentions; e.g., "Some other sounds would've been nice" and "More variety"), loop transitions (2 mentions; e.g., "Regulate the length of the sounds" and "The transition between loops felt disruptive"), and unnatural sounds (1 mention; e.g., "They didn't feel very natural to me").
Figure 8 presents the code cloud for user feedback regarding their interaction with ASMRcade (i.e., Q8).It is composed of three colors: green for positive feedback, red for negative feedback, and yellow for neutral feedback.The codes highlight diferent aspects, including Enjoyable experience (3 mentions; e.g., "It was an enjoyable experience", "It was interesting and was fun to use"), Intuitive interaction (5 mentions; e.g., "The interaction was easy and intuitive", "It was mostly intuitive", "Easy to understand, learning by doing worked fast"), Tutorial issue (2 mentions; e.g., "the Tutorial problem", "Would have liked to be able to pause/click through the tutorial myself"), Unique interaction features (2 mentions; e.g., "I was pleasantly surprised that the system placed the balls at the maximum possible distance when I moved them too far", "Nice imaging of the diferent bubbles as an input vector for the neuronal network"), Interface issues (1 mention; e.g., "Bubbles partly laid over the text"), Potential for improvement (2 mentions; e.g., "More possibilities to customize and play around with diferent sounds would make it very enjoyable").

DISCUSSION
User Experience.Overall, the User Experience Questionnaire (UEQ) results indicate a positive user experience with ASMRcade.The highest mean scores were observed for Novelty, Attractiveness, and Perspicuity, suggesting that ASMRcade was perceived as an innovative and appealing solution with a clear and understandable design.In the open feedback, one user stated, "It's a very interesting experience that I haven't had like that before.I really enjoyed customizing the sounds to what I wanted to hear, which is something I can't do when I'm watching an ASMR video." The relatively lower scores for Efciency, Stimulation, and Dependability indicate room for improvement in these more pragmatic and usability-related scores.The efciency and dependability of the application might be afected by the inherent unpredictability of WaveGAN.Users may need to manipulate multiple spheres to produce a pleasant sound, which might need to be more efcient.One user mentioned, "I felt like manipulating the spheres didn't really change much".
Overall, as the Pragmatic Quality scales (Perspicuity, Efciency, Dependability) had lower mean scores than the Hedonic Quality scales (Stimulation, Originality), one can conclude that participants found ASMRcade to be more enjoyable and stimulating than efcient and dependable.As such, ASMRcade might not be reliable for generating desired sounds on every interaction because of the GANs' inherent unpredictability.This fnding suggests that while the application ofers an engaging and novel experience, its practical aspects may need further refnement to enhance user satisfaction.However, we also note that goal-oriented qualities may be less critical for an application designed for relaxation and de-stressing, such as ASMRcade.Furthermore, higher scores in Hedonic qualities may be attributed to the use case itself, as experiencing ASMR is a relaxing and immersive experience in itself [1,33].One participant's open feedback supports this notion: "It created curiosity and started to engage the creative mind." ASMR Experiences.More than half of the participants reported generally experiencing ASMR, with almost the same proportion experiencing ASMR while interacting with ASMRcade.The overlap between these groups demonstrates ASMRcade's potential to induce ASMR in users susceptible to the phenomenon.However, not all participants who generally experienced ASMR reported experiencing it with ASMRcade, suggesting that the application may need further improvements to cater to a broader range of ASMR triggers and preferences.On the other hand, certain participants were able to experience ASMR that did not experience ASMR before.This observation indicates that the interactive component we introduced through the ASMRcade application might have advantages over passive ASMR consumption.Interacting with an ASMR content generator can even be seen as a new category of ASMR triggers, which, like other categories, may work very well for some users but less for others.Here, future work has to dive more into detail on how exactly interactive and passive ASMR consumption difers in triggering ASMR experiences.The open feedback collected from participants provided valuable insights into their experiences and opinions regarding ASMRcade, which complemented the quantitative data obtained from the User Experience Questionnaire (UEQ).By applying an inductive thematic analysis approach to the open feedback, several recurring themes were identifed that shed light on the strengths and areas for improvement of the application.One of the key strengths of ASMRcade highlighted by the participants was the intuitive interaction.Participants found navigating and manipulating the spheres to create diferent sounds easy.For example, one participant noted that "Changing the sounds became way more intuitive" once they got the hang of the system.This feedback corresponds with the above-average scores for Attractiveness and Perspicuity in the UEQ results.However, participants also mentioned that some aspects of ASMRcade's sphere functions needed clarifcation.One user stated, "I didn't really understand what manipulating the spheres actually does," while another mentioned, "Fine-tuning is a little bit confusing." These comments suggest that some users struggled to comprehend how the sphere manipulation impacted the generated sounds.Addressing this issue by providing clearer explanations about the function and limits of sphere interaction, visual cues, or tooltips could help users better understand the connection between sphere manipulation and the resulting sounds, thus enhancing their overall experience.One way to give visual cues would be to do a Vector Impact Analysis, like Schlagowski et al. did for their drum sequencer [37].After analyzing their impact on the generated sounds, spheres can be sorted accordingly, and shaders could be used to indicate individual spheres' impact on diferent frequency bands.The open feedback also revealed that participants appreciated the sound customization options available in ASMRcade but desired more advanced customization features.Users found the ability to manipulate volume and reverb controls helpful, as one participant noted, "I liked the option that you could separately change the volume and the Reverb" However, some users expressed a desire for even more control over sound characteristics, such as speed and frequency adjustments.For example, one participant suggested, "I would also like there to be a slider where I could manipulate the speed of the sound".Further constructive feedback on areas that could be improved was provided.For instance, several users mentioned that the application would beneft from a more diverse selection of sounds.One participant suggested, "Diversify the sound categories".Another user mentioned that the sound transitions could be smoother, stating, "The transition between the loops was pretty noticeable, which made it feel less natural to me." Furthermore, some participants found certain features to need to be clarifed or to be more intuitive within ASMRcade.For example, one user noted that the tutorial button needed to be bigger and easier to notice.Another participant mentioned difculties understanding the impact of changing the sphere size slider, which hides certain manipulatable spheres: "I didn't understand the size change; I couldn't hear any diference with changing the size." To address these issues, the application could be refned by improving the visibility and accessibility of the tutorial, as well as providing more precise explanations of what the size slider does and how it afects the generated sounds.As the by far most recurring theme was the diversity of sounds, the logical next step should be to train the WaveGAN not only on tapping sounds but on a broad spectrum of diferent ASMR audio triggers that exist.These could include scratching, whispering, or other mouth-made sounds.By addressing these areas, ASMRcade could further enhance user satisfaction and provide a more engaging and enjoyable experience for users seeking relaxation and stress relief through ASMR.Further, it should be noted that our exploratory user study had a relatively small sample size.Future research has to delve more into detail by studying specifc characteristics of interactive ASMR that contribute to the users' experience through quantitative user studies and more participants.

CONCLUSION
In this work, we presented ASMRcade, an application for interactively exploring and generating personalized ASMR audio triggers.Using this application, users could change the input vector for a WaveGAN capable of transforming that input vector to ASMRtriggering tapping sounds by manipulating the synthesized sounds via a web-based user interface.A frst explorative user study indicates that some users can beneft from the interactive approach and that it might be able to broaden accessibility for ASMR experiences as some users had their frst successful ASMR triggers with our system.
From user experience questionnaires and the qualitative feedback, we conclude that users appreciated the intuitive interaction with the application, which was also perceived as innovative and appealing.Users especially enjoyed the sound customization options, but requested further control over the speed and frequency of the generated sound.Furthermore, while the sound quality was good enough, it could be further improved by using a higher sampling rate and stereo signals.Also, many users asked for more variety in the generated sounds.As such, future work has to extend the system to a larger set of ASMR sound categories.
To conclude, the study is a proof of concept for a novel interactive approach for generating ASMR triggers.The substantial share of users that experienced ASMR and their interest in improving ASM-Rcade shows great potential for an interactive ASMR experience as a suitable alternative to conventional ASMR content.
1) or what they thought would have to change to experience ASMR (Q2.2). • Q3: How much time (in minutes) have you spent in the ASMRcade?(Numerical-only free-form input)

Figure 2 :
Figure 2: Mean values and variances for UEQ scales.The plot was created with the UEQ Data Analysis Tool, Version 12.

Figure 3 :
Figure 3: Word cloud resembling the responses to how the users were interacting with the application when ASMR was triggered (Q2.1, green), and what they think would need to change in order to experience ASMR (Q2.2, red).

4. 3 . 1
Demographic Qestions.The demographic analysis of the remaining 15 participants revealed the following:• Age: Participants ranged from 19 to 35 years old, with a mean age of 24.53 years.

Figure 5 :
Figure 5: Word cloud resembling the responses to what users liked (Q5).

Figure 3
Figure 3 presents the code cloud to Q2.1 and Q2.2.Participants were shown one of those questions depending on how they answered regarding experiencing ASMR while interacting with ASM-Rcade.Green words represent feedback on how participants (that reported experiencing ASMR while interacting with ASMRcade) interacted with ASMRcade when their ASMR was triggered.Red words represent participants' feedback that they hadn't experienced ASMR while interacting with ASMRcade about what they thought was missing or needed to change for them to experience ASMR.The feedback by users that had experienced ASMR (green) includes aspects such as intuitive interaction (1 mention; e.g., "Changing the sounds became way more intuitive"), reshufing (2 mentions; e.g., "Reshufing until I experienced it"), associating sounds with daily life (1 mention; e.g., "My brain started to fnd references from my daily life from which the sound could be known and tried to fnd

Figure 6 :
Figure 6: Word clouds resembling the responses to what users disliked (Q6)