Human-generated Sounds Research Articles

Acceptance and Commitment Therapy for an Emerging Adult Female With Misophonia: A Case Study.

Misophonia is characterized by decreased tolerance to idiosyncratic and repetitive human-generated sounds (ie, triggers), coupled with affective-based reactions that result in impairment and distress. Often having its onset in youth, misophonia can become especially prominent during key developmental periods, including emerging adulthood. While efforts to develop interventions for misophonia remain ongoing, a consensus has not yet been reached concerning recommended first-line treatments. Acceptance and commitment therapy (ACT), which has demonstrated efficacy in treating various psychiatric disorders via targeting psychological flexibility processes, represents one potentially feasible approach for addressing misophonia. This case study describes the application of an individually delivered, 12-session ACT intervention for a black female in her early 20s with misophonia. Descriptive data collected at 4 time points (pretreatment, mid-treatment, posttreatment, and at 2-month follow-up) suggested the potential promise of ACT as a treatment for misophonia, improving psychological flexibility processes and reducing secondary depression and anxiety symptoms. Findings are discussed in the context of the possible mechanisms of ACT most likely responsible for misophonia-related clinical improvement. More rigorous studies (eg, clinical trials) are needed to confirm promising findings from existing case studies.

EXPLORING THE CONCEPT OF AMBIENT SOUND FOR WELL-BEING: A SYSTEMATIC REVIEW OF THEORIES AND EVIDENCEON

This study investigates the impact of ambient sound on well-being, focusing on its definition, theoretical foundations, and cultural interpretations. The objectives are to explore and analyse underlying theories and concepts, and to examine cultural perceptions of ambient sound’s influence on human life. The researchers searched multiple databases, including JSTOR, PubMed, and Scopus, using keywords related to ambient sound and its synonyms. A total of 162 articles were examined and only 46 articles were included in the review. The results of this review proposed that definition of ambient sound highlights its role as the comprehensive auditory backdrop that shapes individuals’ perceptions, understanding, and experiences of a specific location, incorporating various elements such as natural sounds, background noises, and human-generated sounds. This definition underscores the significance of ambient sound in shaping the overall sonic atmosphere and cultural interpretation of a particular space. Additionally, the perception of environmental noise varies across cultures, and cultural factors influence the use and acceptance of noise cancellation technology and the significance attributed to natural sounds. Further research is needed to explore the potential benefit of ambient sound as a background sound within a various settings such as therapeutic environment.

Association between Chronic Misophonia-Induced Stress and Gastrointestinal Pathology in Children-A Hypothesis.

Misophonia is a neurophysiological disorder with behavioral implications, is complex and multifactorial in origin, and is characterized by an atypical and disproportionate emotional response to specific sounds or associated visual stimuli. Triggers include human-generated sounds, mainly sounds related to feeding and breathing processes, and repetitive mechanical sounds. In response to the triggering stimulus, the patient experiences immediate, high-intensity, disproportionate physical and emotional reactions that affect their quality of life and social functioning. The symptoms of misophonia can occur at any age, but onset in childhood or adolescence is most common. Affected children live in a constant state of anxiety, suffer continuous physical and emotional discomfort, and are thus exposed to significant chronic stress. Chronic stress, especially during childhood, has consequences on the main biological systems through the dysregulation of the hypothalamic-pituitary-adrenal axis, including the gastrointestinal tract. Here, we provide arguments for a positive correlation between misophonic pathology and gastrointestinal symptoms, and this hypothesis may be the starting point for further longitudinal studies that could investigate the correlations between these childhood vulnerabilities caused by misophonia and their effect on the gastrointestinal system. Further research to study this hypothesis is essential to ensure correct and timely diagnosis and optimal psychological and pharmacological support.

Grounded theory approach on audio-visual perception of users in the restaurant environment

This study aims to investigate the occupants' auditory and visual perception based on varying spectral characteristics of the soundscape in restaurant environment at Bilkent University, Türkiye. The study hypothesized that the sounds generated from background activity evoke a negative perception, whereas human-generated sounds positively affect users. It also hypothesized that there is a linear correlation between sound's spectrum and the negative soundscape and that users sitting adjacent or facing the buffet counter experienced negative soundscape compared to those sitting adjacent or facing the windows with an outside view. To analyze the users' perception of soundscape and visual environment, measurements were taken from other locations based on their auditory and visual differences due to the ceiling heights, the distance between the tables, and their distance from the buffet service counter. Volunteers were interviewed using semi-structured questions, and their responses were evaluated through a grounded theory method via ATLAS. Additionally, the volunteers were requested to complete a questionnaire based on Method A for each location and throughout the experiment. The results showed that the auditory perception of the users does not only depend on sound levels since the responses towards sound sources and sound levels change depending on the audio-visual characteristics of the space.

Soundscape experience of public spaces in different world regions: A comparison between the European and Chinese contexts via a large-scale on-site surveya).

The influence of cultural background on the soundscape experience in public spaces has been widely acknowledged. However, most studies have not used standardized protocols for soundscape perception data collection, nor have they gathered large datasets across different regions of the world to investigate possible cultural differences. This study explored the relationships between soundscape descriptors, perceived dominance of sound sources, and overall soundscape qualities and whether these relationships differ across world regions. A database of over 2000 soundscape surveys was collected in situ in outdoor public spaces in Europe and China. Results highlighted differences in how European and Chinese participants perceived the pleasantness and dominance of different sound sources. Specifically, the positive correlation between perceived pleasantness and natural sounds was stronger for European participants. For Chinese participants, vibrant soundscapes were positively correlated with perceived dominance of natural sounds, whereas in Europe, they were associated more with human-generated sounds. Perceived loudness had a greater effect on the appropriateness dimension for the Chinese sample than that for the European sample. This study provides a deeper understanding of how the geographical/cultural context can influence soundscape perception in public spaces and suggests that such country-specific factors should be considered when designing urban soundscapes.

Principle dimensions of perceptual attributes in indoor public spaces

This study aims to analyze the principal dimensions of perceptual attributes in indoor public spaces. Healthcare, working, cultural, educational, leisure, worship, and transportation spaces (e.g., bus, train, metro stations, and airports) were chosen as public spaces. Three members of the research team independently listened to all 70 binaural recordings, taken from indoor spaces, and for each recording identified whether human-generated sounds (e.g., conversation, laughter, footsteps, and coughing), technology-related sounds (e.g., mechanical and electronic sounds) and/or environmental sounds dominated. University students (n=120) performed a listening test to assess 70 binaural recordings of indoor soundscapes on users' attribute scale. Principal component analysis shows that there are two prominent groups: pleasantness and eventfulness. Perceived sound environments dominated by technology-related sounds were found to be unpleasant, while the acoustic environments dominated by human-generated sounds were eventful.

Sound Quality Factors Inducing the Autonomous Sensory Meridian Response.

The acoustical characteristics of auditory triggers often recommended to generate the autonomous sensory meridian response (ASMR) on Internet platforms were investigated by parameterizing their sound qualities following Zwicker’s procedure and calculating autocorrelation (ACF)/interaural cross-correlation (IACF) functions. For 20 triggers (10 human- and 10 nature-generated sounds), scores (on a five-point Likert scale) of the ASMR, perceived loudness, perceived pitch, comfort, and perceived closeness to the sound image were obtained for 26 participants by questionnaire. The results show that the human-generated sounds were more likely to trigger stronger ASMR than nature-generated sounds, and the primary psychological aspect relating to the ASMR was the perceived closeness, with the triggers perceived more closely to a listener having higher ASMR scores. The perceived closeness was evaluated by the loudness and roughness (among Zwicker’s parameter) for the nature-generated sounds and the interaural cross-correlation coefficient (IACC) (among ACF/IACF parameters) for the human-generated sounds. The nature-generated sounds with higher loudness and roughness and the human-generated sounds with lower IACC were likely to evoke the ASMR sensation.

Categorizing auditory objects of perceived sounds in the built environment

This exploratory study focuses on a cognitive approach to categorize complex auditory scenes in the built environment, while prior studies have concentrated on outdoor acoustic environments. Six experts and 30 non-experts performed a free chip sorting task to assess 70 binaural recordings taken from indoor spaces. Healthcare, working, cultural, educational, leisure, worship, and transportation spaces (e.g., bus, train, metro stations, and airports) were chosen as public spaces. The participants were asked to classify the auditory objects into sound categories based on the descriptive labels provided by the authors to identify the sound sources. The findings, obtained through hierarchical agglomerative clustering and non-metric multidimensional scaling (MDS), show that there are three prominent category labels regarding perceived sound in the indoor acoustic environment: (1) intelligible and unintelligible speech; (2) periodic and transient sounds; and (3) stationary and non-stationary sounds. Human-generated sounds such as conversation, laughter, footsteps, and coughing vary over time according to the context of the built environment. Moreover, technology-related sounds, such as mechanical and electronic ones, have a deterministic and random nature that differ according to the function of the spaces.

COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests. The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people.

Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath

The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. The Artificial Intelligence (AI) based models deployed into the real-world to identify the COVID-19 disease from human-generated sounds such as voice/speech, dry cough, and breath. The CNN (Convolutional Neural Network) is used to solve many real-world problems with Artificial Intelligence (AI) based machines. We have proposed and implemented a multi-channeled Deep Convolutional Neural Network (DCNN) for automatic diagnosis of COVID-19 disease from human respiratory sounds like a voice, dry cough, and breath, and it will give better accuracy and performance than previous models. We have applied multi-feature channels such as the data De-noising Auto Encoder (DAE) technique, GFCC (Gamma-tone Frequency Cepstral Coefficients), and IMFCC (Improved Multi-frequency Cepstral Coefficients) methods on augmented data to extract the deep features for the input of the CNN. The proposed approach improves system performance to the diagnosis of COVID-19 disease and provides better results on the COVID-19 respiratory sound dataset.

Visitor presence and a changing soundscape, alongside environmental parameters, can predict enclosure usage in captive flamingos

The sound environment of a zoo animal is a complex milieu of animal and human-generated sounds; coming from the species itself, other species, visitors, keepers and other zoo-users. Research determining how different components of the sound environment affect animal behaviour is surprisingly lacking but could have real-world impacts for animal welfare and zoo enclosure design. The current study investigated the effects of the sound environment on two flocks of flamingos housed in open-air enclosures at British zoos. Measures of how each flock used its enclosure (as a response variable) and environmental variables (Inband Power and Peak Frequency were recorded as characteristics of the sound environment, as well as temperature, humidity and cloud cover, and finally visitor presence-all as potential predictor variables) were made over a 2-month period. Assessment of space use by zoo animals is often used as a measure of the appropriateness of an exhibit and to understand welfare. Given that flamingo activity is influenced by weather and that the sound environment of the zoo is likely to be influenced by the number and the presence of visitors, it was assumed that these predictor variables would influence where the flamingos were located at different times of the day. As expected, there was a complicated relationship between enclosure use and Inband Power (average spectral density, a measure of sound energy) in both flocks; visitors generated salient sound but other visitor characteristics such as their physical presence may have impacted the movement of the birds around their enclosures. Results show a complex picture where environmental conditions influence flamingo enclosure usage as well as visitor presence and sounds around/in the enclosure. Findings are not consistent between the two flocks, with one flock demonstrating distinct temporal change to enclosure zone occupancy and the other responsive to humidity and cloud cover variation. We believe enclosure use can provide a valuable indication of how birds react to their soundscape; however, our findings suggest more work is needed to unpick the components of captive sound environments, and their relative effects on how animals use their space.

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice.

The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.ResultsAs a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds.ConclusionA Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

Change detection in complex auditory scenes is predicted by auditory memory, pitch perception, and years of musical training

Our world is a sonically busy place and we use both acoustic information and experience-based knowledge to make sense of the sounds arriving at our ears. The knowledge we gain through experience has the potential to shape what sounds are prioritized in a complex scene. There are many examples of how visual expertise influences how we perceive objects in visual scenes, but few studies examine how auditory expertise is associated with attentional biases toward familiar real-world sounds in complex scenes. In the current study, we investigated whether musical expertise is associated with the ability to detect changes to real-world sounds in complex auditory scenes, and whether any such benefit is specific to musical instrument sounds. We also examined whether change detection is better for human-generated sounds in general or only communicative human sounds. We found that musicians had less change deafness overall. All listeners were better at detecting human communicative sounds compared to human non-communicative sounds, but this benefit was driven by speech sounds and sounds that were vocally generated. Musical listening skill, speech-in-noise, and executive function abilities were used to predict rates of change deafness. Auditory memory, musical training, fine-grained pitch processing, and an interaction between training and pitch processing accounted for 45.8% of the variance in change deafness. To better understand perceptual and cognitive expertise, it may be more important to measure various auditory skills and relate them to each other, as opposed to comparing experts to non-experts.

UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources

Advancements in oceanic research have resulted in a plethora of activities such as undersea oil/gas exploration, environmental monitoring, sonar-based coastal surveillance, which have each increased the acoustic noise levels in the ocean and have raised concerns in the scientific community about the effect of human-generated sounds on marine life. Knowledge of the statistical characteristics of noise sources and their spatial distribution is paramount for understanding the impact on marine life as well as for regulating and policing such activities. Furthermore, studies have shown that assuming the underwater noise probability density function to be Gaussian, exponential, or Weibull is often not valid; therefore, statistically profiling the sources of the ambient noise is also essential to improve the performance of acoustic communication systems in the harsh underwater environment. In this paper, a novel solution based on the blind source separation method is proposed to enable separation of underwater acoustic noise point sources in the presence of channel propagation multipath. The proposed Underwater Noise Inspection, Separation, and Classification (UNISeC) system performs several pre- and postprocessing steps forming a novel gray-box model. Assuming there is no prior information on the noise sources, UNISeC estimates the number of such sources as well as characterizes and classifies them via a recursive pilot-aided probing method while minimizing the environmental acoustic contamination. A correlation-based characterization as well as power spectral density based classification approaches are investigated to verify the proposed method. Several scenarios are also presented and evaluated in detail via simulations.

Natural and anthropogenic sounds reduce song performance: insights from two emberizid species

Lay SummaryWe studied the effects of energy sector and ocean surf sounds on male song performance, an important attribute in assessing their quality. We found that both ocean and human-generated sounds are linked to reduced song performance. Our results demonstrate that natural sounds can influence avian vocal behaviour in ways similar to human-made sounds and, despite having song characteristics that may be heard better in loud areas, the quality of the signal may be compromised.

Case study: A novel application of mindfulness- and acceptance-based components to treat misophonia

Misophonia is an important, yet understudied, psychological condition characterized by feelings of extreme anger and disgust in response to specific human-generated sounds. Several promising case studies using cognitive behavioral therapy to treat misophonia have been published, but given the limited work to date, exploring additional treatment options and expanding the potential options available to clients and clinicians remains important. In order to target the high levels of anger and disgust, we treated a case of misophonia in a 17-year-old male using 10 (50-min) individual sessions based on mindfulness- and acceptance-based components drawn from dialectical behavior therapy and acceptance and commitment therapy. In particular, we focused on acceptance, mindfulness, opposite action, and nonjudgmentalness strategies. At 6-month follow-up, the client reported no significant difficulties and a continued decline in symptoms. Theoretical rationale and treatment implications are discussed.

Behavioral changes in response to sound exposure and no spatial avoidance of noisy conditions in captive zebrafish.

Auditory sensitivity in fish serves various important functions, but also makes fish susceptible to noise pollution. Human-generated sounds may affect behavioral patterns of fish, both in natural conditions and in captivity. Fish are often kept for consumption in aquaculture, on display in zoos and hobby aquaria, and for medical sciences in research facilities, but little is known about the impact of ambient sounds in fish tanks. In this study, we conducted two indoor exposure experiments with zebrafish (Danio rerio). The first experiment demonstrated that exposure to moderate sound levels (112 dB re 1 μPa) can affect the swimming behavior of fish by changing group cohesion, swimming speed and swimming height. Effects were brief for both continuous and intermittent noise treatments. In the second experiment, fish could influence exposure to higher sound levels by swimming freely between an artificially noisy fish tank (120–140 dB re 1 μPa) and another with ambient noise levels (89 dB re 1 μPa). Despite initial startle responses, and a brief period in which many individuals in the noisy tank dived down to the bottom, there was no spatial avoidance or noise-dependent tank preference at all. The frequent exchange rate of about 60 fish passages per hour between tanks was not affected by continuous or intermittent exposures. In conclusion, small groups of captive zebrafish were able to detect sounds already at relatively low sound levels and adjust their behavior to it. Relatively high sound levels were at least at the on-set disturbing, but did not lead to spatial avoidance. Further research is needed to show whether zebrafish are not able to avoid noisy areas or just not bothered. Quantitatively, these data are not directly applicable to other fish species or other fish tanks, but they do indicate that sound exposure may affect fish behavior in any captive condition.

The effects of human‐generated sound on fish

There is growing international concern about the effects of human-generated sound on fish and other aquatic organisms. However, because of a striking paucity of well-designed and controlled experimental data, very little is actually known about the effects of these sounds on fish. Findings suggest that human-generated sounds, even from very high intensity sources, might have no effect in some cases or might result in effects that range from small and temporary shifts in behavior all the way to immediate death. At this point, however, it is nearly impossible to extrapolate from results with one sound source, one fish species, or even fish of one size to other sources, species, or fish sizes. The present paper briefly discusses the potential effects of sound on fish, describes some of the more recent well-controlled experimental studies, and points out areas for future study that will be needed before a real understanding of the effects of sound on fish can be developed.