Childhood sound disturbance and sleep problems in Alpine valleys with high levels of traffic exposures and greenspace.

Sound disturbance and sleep problems are regarded as the most common adverse effects of environmental noise but evidence of the role of air pollution and greenspace is scant. This is especially true for children who find themselves in a sensitive developmental period and experience their environment differently than adults. This study examined the joint effects of traffic exposures and residential greenspace on child sound disturbance and sleep problems via perceptions of neighborhood quality. We used cross-sectional data for 1251 schoolchildren (8-12 years) in the Tyrol region of Austria/Italy. Questionnaires provided information on sociodemographic and housing factors, perceived neighborhood quality, sound disturbance in different situations, and sleep problems. Modelled acoustic indicators included day-evening-night sound levels and the highest percentile level, and night-time sound level and a bespoke sleep disturbance index. Nitrogen dioxide served as a proxy for traffic-related air pollution. The normalized difference vegetation index was calculated as a measure of residential greenspace, and presence of a domestic garden was self-reported. Results showed that higher level of traffic-related exposures was positively associated with sound disturbance and sleep problems, while living in a greener area, especially in a house with a garden, was associated with lower sound disturbance and less sleep problems even in the presence of traffic. Traffic exposures contributed to more unfavorable, and greenspace to more positive perceptions in terms of traffic-related stressors, opportunities for outdoor recreation, and general satisfaction with the neighborhood. This indirect path seemed more important for greenspace than for traffic exposures. In conclusion, it seems advantageous to combine traffic-related mitigation with improving access to greenspace in interventions for supporting the acoustic comfort of children during day and nighttime. Even highly nature-dominated environments could still benefit from proximal green infrastructure, especially from domestic gardens.

Time course of EEG complexity reflects attentional engagement during listening to speech in noise.

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope.

PURPOSE: The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection? METHODS: A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.5 months, with increased frequency during exacerbations) performed self-examinations using 3 devices: an artificial intelligence (AI)-aided home stethoscope (providing wheezes, rhonchi, and coarse and fine crackles intensity; respiratory and heart rate; and inspiration-to-expiration ratio), a peripheral capillary oxygen saturation (SpO2) meter, and a peak expiratory flow (PEF) meter and filled out a health state survey. The resulting 6,029 examinations were evaluated by physicians for the presence of exacerbations. For each registered parameter, a machine learning model was trained, and the area under the receiver operating characteristic curve (AUC) was calculated to assess its utility in exacerbation detection. RESULTS: The best single-parameter discriminators of exacerbations were wheezes intensity for young children (AUC 84% [95% CI, 82%-85%]), rhonchi intensity for older children (AUC 81% [95% CI, 79%-84%]), and survey answers for adults (AUC 92% [95% CI, 89%-95%]). The greatest efficacy (in terms of AUC) was observed for a combination of several parameters. CONCLUSIONS: The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier.

Protective effect of restorative possibilities on cognitive function and mental health in children and adolescents: A scoping review including the role of physical activity.

Background The exposome approach can be a powerful tool for understanding the intertwining of social, physical, and internal influences that shape mental health and cognitive development throughout childhood. To distil conceptual models for subsequent analyses, the EU-funded project Early Environmental quality and Life-course mental health effects (Equal-Life) has conducted literature reviews on potential mediators linking the exposome to these outcomes. We report on a scoping review and a conceptual model of the role of restorative possibilities and physical activity. Methods Peer-reviewed studies published since the year 2000 in English, on the association between the exposome and mental health/cognition in children/adolescents, and quantitatively investigating restoration/restorative quality as a mediating variable were considered. Database searches were last updated in December 2022. We used an unstructured expert-driven approach to fill in gaps in the reviewed literature. Results Five records of three distinct studies were identified, indicating a scarcity of empirical evidence in this newly developing research area. Not only were these studies few in numbers, but also cross-sectional, lending only tentative support to the idea that perceived restorative quality of adolescent's living environment might mediate the association between greenspace and mental health. Physical activity emerged as a mediator leading to better psychological outcomes in restorative environments. We provide a critical discussion of potential caveats when investigating the restoration mechanism in children and propose a hierarchical model including restoration, physical activity, and relational dynamics between children and their environment, including social context, as well as restorative environments other than nature. Conclusions It is justified to further explore the role of restoration and physical activity as mediators in the association between early-life exposome and mental health/cognitive development. It is important to consider the child perspective and specific methodological caveats. Given the evolving conceptual definitions/operationalizations, Equal-Life will attempt to fill in a critical gap in the literature.

Adopting a child perspective for exposome research on mental health and cognitive development - Conceptualisation and opportunities.

Mental disorders among children and adolescents pose a significant global challenge. The exposome framework covering the totality of internal, social and physical exposures over a lifetime provides opportunities to better understand the causes of and processes related to mental health, and cognitive functioning. The paper presents a conceptual framework on exposome, mental health, and cognitive development in children and adolescents, with potential mediating pathways, providing a possibility for interventions along the life course. The paper underscores the significance of adopting a child perspective to the exposome, acknowledging children's specific vulnerability, including differential exposures, susceptibility of effects and capacity to respond; their susceptibility during development and growth, highlighting neurodevelopmental processes from conception to young adulthood that are highly sensitive to external exposures. Further, critical periods when exposures may have significant effects on a child's development and future health are addressed. The paper stresses that children's behaviour, physiology, activity pattern and place for activities make them differently vulnerable to environmental pollutants, and calls for child-specific assessment methods, currently lacking within today's health frameworks. The importance of understanding the interplay between structure and agency is emphasized, where agency is guided by social structures and practices and vice-versa. An intersectional approach that acknowledges the interplay of social and physical exposures as well as a global and rural perspective on exposome is further pointed out. To advance the exposome field, interdisciplinary efforts that involve multiple scientific disciplines are crucial. By adopting a child perspective and incorporating an exposome approach, we can gain a comprehensive understanding of how exposures impact children's mental health and cognitive development leading to better outcomes.

AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea).

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition. Few attempts were made to include appraisal (e.g., in circumplex frameworks). This paper proposes an artificial intelligence (AI)-based dual-branch convolutional neural network with cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape characterization, including sound recognition and appraisal. Using the DeLTA dataset containing human-annotated sound source labels and perceived annoyance, the DCNN-CaF is proposed to perform sound source classification (SSC) and human-perceived annoyance rating prediction (ARP). Experimental findings indicate that (1) the proposed DCNN-CaF using loudness and Mel features outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with cross-attention fusion outperforms other typical AI-based models and soundscape-related traditional machine learning methods on the SSC and ARP tasks. (3) Correlation analysis reveals that the relationship between sound sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF model. (4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on soundscape augmentation.

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques-support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)-to achieve low-latency risk-taking behaviour prediction during human-robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (R2), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an R2 of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30 R2. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human-robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human-robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human-robot tactile interaction.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique-an artificial neural network in this work-is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2-3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject's electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal-occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain-computer interfaces. Furthermore, they validate the EEG data collected in our experiment.

Parkinson's disease affects the neural alpha oscillations associated with speech-in-noise processing.

Parkinson's disease (PD) has increasingly been associated with auditory dysfunction, including alterations regarding the control of auditory information processing. Although these alterations may interfere with the processing of speech in degraded listening conditions, behavioural studies have generally found preserved speech-in-noise recognition in PD. However, behavioural speech audiometry does not capture the neurophysiological mechanisms supporting speech-in-noise processing. Therefore, the aim of this study was to investigate the neural oscillatory mechanisms associated with speech-in-noise processing in PD. Twelve persons with PD and 12 age- and gender-matched healthy controls (HCs) were included in this study. Persons with PD were studied in the medication off condition. All subjects underwent an audiometric screening and performed a sentence-in-noise recognition task under simultaneous electroencephalography (EEG) recording. Behavioural speech recognition scores and self-reported ratings of effort, performance, and motivation were collected. Time-frequency analysis of EEG data revealed no significant difference between persons with PD and HCs regarding delta-theta (2-8 Hz) inter-trial phase coherence to noise and sentence onset. In contrast, significantly increased alpha (8-12 Hz) power was found in persons with PD compared with HCs during the sentence-in-noise recognition task. Behaviourally, persons with PD demonstrated significantly decreased speech recognition scores, whereas no significant differences were found regarding effort, performance, and motivation ratings. These results suggest that persons with PD allocate more cognitive resources to support speech-in-noise processing. The interpretation of this finding is discussed in the context of a top-down mediated compensation mechanism for inefficient filtering and degradation of auditory input in PD.

Evaluation of multi-feature auditory deviance detection in Parkinson's disease: a mismatch negativity study.

Behavioral studies on auditory deviance detection in patients with Parkinson's disease (PD) have reported contradictory results. The primary aim of this study was to investigate auditory deviance detection of multiple auditory features in patients with PD by means of objective and reliable electroencephalographic (EEG) measurements. Twelve patients with early-stage PD and twelve age- and gender-matched healthy controls (HCs) were included in this study. Patients with PD participated without their regular dopaminergic medication. All subjects underwent an audiometric screening and performed a passive multi-feature mismatch negativity (MMN) paradigm. Repeated-measures analysis of variance (ANOVA) demonstrated no significant differences between patients with PD and HCs regarding MMN mean amplitude and latency for frequency, duration and gap deviants. Nevertheless, a trend towards increased MMN mean amplitude and latency was found in response to intensity deviants in patients with PD compared to HCs. Increased intensity MMN amplitude may indicate that more neural resources are allocated to the processing of intensity deviances in patients with PD compared to HCs. The interpretation of this intensity-specific MMN alteration is further discussed in the context of a compensatory mechanism for auditory intensity processing and involuntary attention switching in PD.

Neurophysiological investigation of auditory intensity dependence in patients with Parkinson's disease.

There is accumulating evidence for auditory dysfunctions in patients with Parkinson's disease (PD). Moreover, a possible relationship has been suggested between altered auditory intensity processing and the hypophonic speech characteristics in PD. Nonetheless, further insight into the neurophysiological correlates of auditory intensity processing in patients with PD is needed primarily. In the present study, high-density EEG recordings were used to investigate intensity dependence of auditory evoked potentials (IDAEPs) in 14 patients with PD and 14 age- and gender-matched healthy control participants (HCs). Patients with PD were evaluated in both the on- and off-medication states. HCs were also evaluated twice. Significantly increased IDAEP of the N1/P2 was demonstrated in patients with PD evaluated in the on-medication state compared to HCs. Distinctive results were found for the N1 and P2 component. Regarding the N1 component, no differences in latency or amplitude were shown between patients with PD and HCs regardless of the medication state. In contrast, increased P2 amplitude was demonstrated in patients with PD evaluated in the on-medication state compared to the off-medication state and HCs. In addition to a dopaminergic deficiency, deficits in serotonergic neurotransmission in PD were shown based on increased IDAEP. Due to specific alterations of the N1-P2 complex, the current results suggest deficiencies in early-attentive inhibitory processing of auditory input in PD. This interpretation is consistent with the involvement of the basal ganglia and the role of dopaminergic and serotonergic neurotransmission in auditory gating.

Analysis of probe fitting stimulus properties on transient evoked otoacoustic emissions.

Objective: The objectives of the current study are first, to evaluate the subjective evaluation of probe fitting stimulus properties, and second, to analyse the effects of different probe fittings on transient evoked otoacoustic emission (TEOAE) response and noise amplitudes in subjects.Design: The Q methodology was used to sort 48 probe fittings differing in stimulus properties in seven categories from totally unacceptable to certainly acceptable. Further, TEOAE response and noise amplitudes were measured in one baseline condition with optimal probe fitting and eight experimental conditions with less than optimal probe fittings.Study sample: The probe fittings were ranked by 18 participants, while the repeated measures design was performed in 34 ear.Results: First, it was found that only 19.61% of all distributions of the probe fittings by the participants had a mutual correlation of at least 70.00%. Almost 60% of the variance of distributions was explained by 83.33% of the participants, although most probe fittings significantly differed from other fittings based on spectral broadness. Second, significant differences in TEOAE response and especially noise amplitudes between conditions were found. Further, TEOAE response and noise amplitudes between the baseline and experimental conditions were significantly different depending on ringing and spectral flatness of the stimulus.Conclusion: A substantial amount of subjectivity during TEOAE measurements is involved with regard to the evaluation of probe fitting stimulus properties. TEOAE response but especially noise amplitudes are influenced by varying stimulus parameters which stresses the importance of inspecting these parameters prior to or during EOAE measurements. Although more research is needed, some guidelines regarding these parameters are given which could improve the accuracy of TEOAEs in practice.

Mobile phones: A trade-off between speech intelligibility and exposure to noise levels and to radio-frequency electromagnetic fields.

When making phone calls, cellphone and smartphone users are exposed to radio-frequency (RF) electromagnetic fields (EMFs) and sound pressure simultaneously. Speech intelligibility during mobile phone calls is related to the sound pressure level of speech relative to potential background sounds and also to the RF-EMF exposure, since the signal quality is correlated with the RF-EMF strength. Additionally, speech intelligibility, sound pressure level, and exposure to RF-EMFs are dependent on how the call is made (on speaker, held at the ear, or with headsets). The relationship between speech intelligibility, sound exposure, and exposure to RF-EMFs is determined in this study. To this aim, the transmitted RF-EMF power was recorded during phone calls made by 53 subjects in three different, controlled exposure scenarios: calling with the phone at the ear, calling in speaker-mode, and calling with a headset. This emitted power is directly proportional to the exposure to RF EMFs and is translated into specific absorption rate using numerical simulations. Simultaneously, sound pressure levels have been recorded and speech intelligibility has been assessed during each phone call. The results show that exposure to RF-EMFs, quantified as the specific absorption in the head, will be reduced when speaker-mode or a headset is used, in comparison to calling next to the ear. Additionally, personal exposure to sound pressure is also found to be highest in the condition where the phone is held next to the ear. On the other hand, speech perception is found to be the best when calling with a phone next to the ear in comparison to the other studied conditions, when background noise is present.

Presenting and processing information in background noise: A combined speaker-listener perspective.

Transferring information orally in background noise is challenging, for both speaker and listener. Successful transfer depends on complex interaction between characteristics related to listener, speaker, task, background noise, and context. To fully assess the underlying real-life mechanisms, experimental design has to mimic this complex reality. In the current study, the effects of different types of background noise have been studied in an ecologically valid test design. Documentary-style information had to be presented by the speaker and simultaneously acquired by the listener in four conditions: quiet, unintelligible multitalker babble, fluctuating city street noise, and little varying highway noise. For both speaker and listener, the primary task was to focus on the content that had to be transferred. In addition, for the speakers, the occurrence of hesitation phenomena was assessed. The listener had to perform an additional secondary task to address listening effort. For the listener the condition with the most eventful background noise, i.e., fluctuating city street noise, appeared to be the most difficult with markedly longer duration of the secondary task. In the same fluctuating background noise, speech appeared to be less disfluent, suggesting a higher level of concentration from the speaker's side.

Kriging-based spatial interpolation from measurements for sound level mapping in urban areas.

Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. Mobile measurements have been performed while walking multiple times in every street of the XIIIth district of Paris. By adaptively constructing a noise map on the basis of these measurements, the role of the density of observations and the performance of four different interpolation strategies is investigated. Ordinary and universal Kriging methods are assessed, as well as the effect of using an alternative definition of the distance between observation locations, which takes the topology of the road network into account. The results show that a high density of observation points is necessary to obtain an interpolated sound map close to the reference map.

Effects of Offshore Wind Farms on the Early Life Stages of Dicentrarchus labrax.

Anthropogenically generated underwater noise in the marine environment is ubiquitous, comprising both intense impulse and continuous noise. The installation of offshore wind farms across the North Sea has triggered a range of ecological questions regarding the impact of anthropogenically produced underwater noise on marine wildlife. Our interest is on the impact on the "passive drifters," i.e., the early life stages of fish that form the basis of fish populations and are an important prey for pelagic predators. This study deals with the impact of pile driving and operational noise generated at offshore wind farms on Dicentrarchus labrax (sea bass) larvae.

Simplified analytical model for sound level prediction at shielded urban locations involving multiple diffraction and reflections.

Accurate and efficient prediction of the sound field in shadow zones behind obstacles is a challenging task but essential to produce urban noise maps. A simplified method is presented to predict sound levels at shielded urban locations, including multi-edge diffraction over successive buildings and multiple reflections between parallel façades. The model is essentially based on Pierce's diffraction theory, where the Fresnel Integral is approximated by trigonometric functions for efficient evaluation, and parameterized for urban environments. The model has been validated for idealized urban configurations by comparing to the results of Pierce's theory and a full-wave numerical method. In case of multi-edge diffraction over buildings in absence of a source or receiver canyon, deviations from the full-wave simulations are smaller than 2 dB for the octave bands with central frequencies ranging from 125 to 1000 Hz. However, larger errors are made when receivers are close to the extension line from the diffraction edge closest to the receiver. In case of combining the simplified multi-edge diffraction model with an efficient approach for including the series of mirror sources and mirror receivers, based on the Hurwitz-Lerch transcendent, this same accuracy is obtained.

Musician earplugs: Appreciation and protection.

Recreational music exposure is a potential risk factor for noise-induced hearing loss (NIHL). Augmented hearing protectors have been designed with modified attenuation characteristics to combine hearing protection and listening comfort. However, to date, only a few independent studies have assessed the performance of those augmented protectors in realistic exposure conditions. This study compares the listening experience and temporary effects on cochlear status with different types of earplugs after exposure to contemporary club music. Five different types of commercially available hearing protectors were worn, all commonly used during leisure-time music exposure. Four of them were augmented premolded earplugs and the fifth type was an inexpensive, standard earplug frequently distributed for free at music events. During five different test sessions of 30 min each, participants not professionally involved in music wore one particular type of protector. Contemporary club music was played at sound pressure levels (SPLs) representative of concerts and bars. After each listening session, a questionnaire on sound quality and general appreciation was completed. In addition, otoacoustic emissions (OAEs) were measured directly before and after music exposure. The reported appreciation clearly differed depending on the addressed characteristics and the specific earplug type. In this test group, the reported appreciation mainly depended on comfort and looks, while differences in sound quality were less noticeable. The changes in OAE amplitude before and after noise exposure were small in terms of clinical standards. Nevertheless, the observed temporary shifts differed systematically for the different types of hearing protectors, with two types of musician earplug showing a more systematic decline than the others. Further research with respect to actual use and achieved protection for real, unsupervised music exposure is warranted.

Comparison of the effects of reef and anthropogenic soundscapes on oyster larvae settlement.

Settlement is a critical period in the life cycle of marine invertebrates with a planktonic larval stage. For reef-building invertebrates such as oysters and corals, settlement rates are predictive for long-term reef survival. Increasing evidence suggests that marine invertebrates use information from ocean soundscapes to inform settlement decisions. Sessile marine invertebrates with a planktonic stage are particularly reliant on environmental cues to direct them to ideal habitats. As gregarious settlers, oysters prefer to settle amongst members of the same species. It has been hypothesized that oyster larvae from species Crassostrea virginica and Ostrea angasi use distinct conspecific oyster reef sounds to navigate to ideal habitats. In controlled laboratory experiments we exposed Pacific Oyster Magallana gigas larvae to anthropogenic sounds from conspecific oyster reefs, vessels, combined reef-vessel sounds as well as off-reef and no speaker controls. Our findings show that sounds recorded at conspecific reefs induced higher percentages of settlement by about 1.44 and 1.64 times compared to off-reef and no speaker controls, respectively. In contrast, the settlement increase compared to the no speaker control was non-significant for vessel sounds (1.21 fold), combined reef-vessel sounds (1.30 fold), and off-reef sounds (1.18 fold). This study serves as a foundational stepping stone for exploring larval sound feature preferences within this species.