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Loudness dependence of auditory evoked potentials (LDAEP) evaluates loudness processing in the human auditory system and is often altered in patients with psychiatric disorders. Previous research has suggested that this measure may be used as an indicator of the central serotonergic system through the highly serotonergic innervation of the auditory cortex. However, differences among the commonly used analysis approaches (such as source analysis and single electrode estimation) may lead to different results. Putatively due to discrepancies of the underlying structures being measured. Therefore, it is important to learn more about how and where in the brain loudness variation is processed. We conducted a detailed investigation of the LDAEP generators and their temporal dynamics by means of multichannel magnetoencephalography (MEG). Evoked responses to brief tones of five different intensities were recorded from 19 healthy participants. We used magnetic field tomography in order to appropriately localize superficial as well as deep source generators of which we conducted a time series analysis. The results showed that apart from the auditory cortex other cortical sources exhibited activation during the N1/P2 time window. Analysis of time courses in the regions of interest revealed a sequential cortical activation from primary sensory areas, particularly the auditory and somatosensory cortex to posterior cingulate cortex (PCC) and to premotor cortex (PMC). The additional activation within the PCC and PMC has implications on the analysis approaches used in LDAEP research. •The loudness dependence of AEP is a potential biomarker for serotonergic function.•We investigated neural generators of N1m/P2m during loudness processing with MEG.•Whole-brain analysis was carried out using magnetic field tomography.•Extra-auditory areas were involved in loudness processing.•These findings have implications on the analysis of the LDAEPs.

The auditory kappa effect is a tendency to base the perceived duration of an inter-onset interval (IOI) separating two sequentially presented sounds on the degree of relative pitch distance separating them. Previous research has found that the degree of frequency discrepancy between tones extends the subjective duration of the IOI. In Experiment 1, auditory kappa effects for sound intensity were tested using a three-tone, AXB paradigm (where the intensity of tone X was shifted to be closer to either Tone A or B). Tones closer in intensity level were perceived as occurring closer in time, evidence of an auditory-intensity kappa effect. In Experiments 2 and 3, the auditory motion hypothesis was tested by preceding AXB patterns with null intensity and coherent intensity context sequences, respectively. The auditory motion hypothesis predicts that coherent sequences should enhance the perception of motion and increase the strength of kappa effects. In this study, the presence of context sequences reduced kappa effect strength regardless of the properties of the context tones.

Purpose: In view of previous findings (Bor, Souza, & Wright, 2008) that some listeners are more susceptible to spectral changes from multichannel compression (MCC) than others, this study addressed the extent to which differences in effects of MCC were related to differences in auditory filter width. Method: Listeners were recruited in 3 groups: listeners with flat sensorineural loss, listeners with sloping sensorineural loss, and a control group of listeners with normal hearing. Individual auditory filter measurements were obtained at 500 and 2000 Hz. The filter widths were related to identification of vowels processed with 16-channel MCC and with a control (linear) condition. Results: Listeners with flat loss had broader filters at 500 Hz but not at 2000 Hz compared with listeners with sloping loss. Vowel identification was poorer for MCC compared with linear amplification. Listeners with flat loss made more errors than listeners with sloping loss, and there was a significant relationship between filter width and the effects of MCC. Conclusions: Broadened auditory filters can reduce the ability to process amplitude-compressed vowel spectra. This suggests that individual frequency selectivity is a factor that influences benefit of MCC when a high number of compression channels are used.

A pupillary dilation response is known to be evoked by salient deviant or contrast auditory stimuli, but so far a direct link between it and subjective salience has been lacking. In two experiments, participants listened to various environmental sounds while their pupillary responses were recorded. In separate sessions, participants performed subjective pairwise-comparison tasks on the sounds with respect to their salience, loudness, vigorousness, preference, beauty, annoyance, and hardness. The pairwise-comparison data were converted to ratings on the Thurstone scale. The results showed a close link between subjective judgments of salience and loudness. The pupil dilated in response to the sound presentations, regardless of sound type. Most importantly, this pupillary dilation response to an auditory stimulus positively correlated with the subjective salience, as well as the loudness, of the sounds (Exp. 1). When the loudnesses of the sounds were identical, the pupil responses to each sound were similar and were not correlated with the subjective judgments of salience or loudness (Exp. 2). This finding was further confirmed by analyses based on individual stimulus pairs and participants. In Experiment 3, when salience and loudness were manipulated by systematically changing the sound pressure level and acoustic characteristics, the pupillary dilation response reflected the changes in both manipulated factors. A regression analysis showed a nearly perfect linear correlation between the pupillary dilation response and loudness. The overall results suggest that the pupillary dilation response reflects the subjective salience of sounds, which is defined, or is heavily influenced, by loudness.

•A transformer model achieved remarkable accuracy in differentiating sounds perceived as “too loud” and “not too loud”.•Attention maps revealed the significance of crucial time windows, which remained obscure in a classical average-based ERP analysis.•This approach promises not only enhanced EEG data analysis in audiology but also more detailed insights into the underlying neural mechanisms. The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signal of multiple trials to extract valuable neural signals from the high noise content of EEG data. However, this averaging technique may conceal relevant information. Our investigation focuses on determining whether a transformer-based deep learning approach, specifically utilizing attention maps, an essential component of transformer networks, can provide deeper insights into ERP data compared to traditional averaging-based analyses. We investigated the data of an experiment on loudness perception. In the study, 29 normal-hearing participants between 18 and 30 years were presented with acoustic stimuli at five different sound levels between 65 and 95 dB and provided their subjective loudness rating, which was categorized as ”too loud” and ”not too loud”. During the sound presentation, EEG signals were recorded. A convolutional transformer was trained to categorize the EEG data into the two classes (”not too loud” and ”too loud”). The classifier exhibited exceptional performance, achieving over 86 % accuracy and an Area under the Curve (AUC) of up to 0.95. Through the utilization of the trained networks, attention maps were generated. Those attention maps provided insights into the time windows relevant for classification within the EEG data. The attention maps above all showed a focus on the time window around 150 to 200 ms, where the average based analysis did not indicate relevant potentials. Employing these attention maps, we were able to gain new perspectives on the ERPs, discovering the attention maps potential as a tool for delving deeper into the analysis of event-related potentials.

•P300 potential as a marker for the subjective loudness perception.•Event-related potentials show a relation to uncomfortably loud stimuli.•The P300 potential could serve as a tool for objectively assessing discomfort levels in infants or adult people who cannot self-report. In this study, the objectification of the subjective perception of loudness was investigated using electroencephalography (EEG). In particular, the emergence of objective markers in the domain of the acoustic discomfort threshold was examined. A cohort of 27 adults with normal hearing, aged between 18 and 30, participated in the study. The participants were presented with 500 ms long noise stimuli via in-ear headphones. The acoustic signals were presented with sound levels of [55, 65, 75, 85, 95 dB]. After each stimulus, the subjects provided their subjective assessment of the perceived loudness using a colored scale on a touchscreen. EEG signals were recorded, and afterward, event-related potentials (ERPs) locked to sound onset were analyzed. Our findings reveal a linear dependency between the N100 component and both the sound level and the subjective loudness categorization of the sound. Additionally, the data demonstrated a nonlinear relationship between the P300 potential and the sound level as well as for the subjective loudness rating. The P300 potential was elicited exclusively when the stimuli had been subjectively rated as ”very loud” or ”too loud”. The findings of the present study suggest the possibility of the identification of the subjective uncomfortable loudness level by objective neural parameters.

Previous studies reported large individual differences in binaural broadband loudness summation in hearing-impaired listeners after narrowband loudness was normalized. These differences in loudness perception might require substantial fine-tuning for some hearing aid users to provide acceptable loudness in daily use. The present study aims at characterizing binaural broadband loudness summation for a hearing-impaired population, the prevalence of higher-than-normal values, and the potential implications for hearing aid target gains. For 180 hearing-impaired participants we measured standard audiological diagnostic parameters, binaural broadband loudness summation and computed gain targets according to NAL-NL2, DSLm[i/o] and trueLOUDNESS, a prescriptive procedure that includes individual loudness measurements. The observed binaural broadband loudness summation of the hearing-impaired participants was, on average, 13 dB higher and showed a higher variance relative to a normal-hearing reference group. In about 40% of all participants a binaural broadband loudness summation was beyond the normal-hearing range. The average excess loudness summation appears to be included in established prescriptive procedures, while relevant differences to trueLOUDNESS gain targets were observed in more than half of the participants. Elevated binaural broadband loudness summation appears to be a prevalent and individual trait in the hearing-impaired population and its assessment might be useful for individualized hearing aid fitting.

The psychological process how tinnitus loudness leads to tinnitus distress remains unclear. This cross-sectional study investigated the mediating role of the emotional state “stress level” and of the two components of the emotional state “arousal” and “valence” with N = 658 users of the “TrackYourTinnitus” smartphone application. Stress mediated the relationship between tinnitus loudness and tinnitus distress in a simple mediation model and even in a multiple mediation model when arousal and valence were held constant. Arousal mediated the loudness-distress relationship when holding valence constant, but not anymore when controlling for valence as well as for stress. Valence functioned as a mediator when controlling for arousal and even when holding arousal and stress constant. The direct effect of tinnitus loudness on tinnitus distress remained significant in all models. This study demonstrates that emotional states affect the process how tinnitus loudness leads to tinnitus distress. We thereby could show that the mediating influence of emotional valence is at least equally strong as the influence of stress. Implications of the findings for future research, assessment and clinical management of tinnitus are discussed.

Novel electric air transportation is emerging as an industry that could help to improve the lives of people living in both metropolitan and rural areas through integration into infrastructure and services. However, as this new resource of accessibility increases in momentum, the need to investigate any potential adverse health impacts on the public becomes paramount. This paper details research investigating the effectiveness of available noise metrics and sound quality metrics (SQMs) for assessing perception of drone noise. A subjective experiment was undertaken to gather data on human response to a comprehensive set of drone sounds and to investigate the relationship between perceived annoyance, perceived loudness and perceived pitch and key psychoacoustic factors. Based on statistical analyses, subjective models were obtained for perceived annoyance, loudness and pitch of drone noise. These models provide understanding on key psychoacoustic features to consider in decision making in order to mitigate the impact of drone noise. For the drone sounds tested in this paper, the main contributors to perceived annoyance are perceived noise level (PNL) and sharpness; for perceived loudness are PNL and fluctuation strength; and for perceived pitch are sharpness, roughness and Aures tonality. Responses for the drone sounds tested were found to be highly sensitive to the distance between drone and receiver, measured in terms of height above ground level (HAGL). All these findings could inform the optimisation of drone operating conditions in order to mitigate community noise.

The underlying mechanism of how the human brain solves the cocktail party problem is largely unknown. Recent neuroimaging studies, however, suggest salient temporal correlations between the auditory neural response and the attended auditory object. Using magnetoencephalography (MEG) recordings of the neural responses of human subjects, we propose a decoding approach for tracking the attentional state while subjects are selectively listening to one of the two speech streams embedded in a competing-speaker environment. We develop a biophysically-inspired state-space model to account for the modulation of the neural response with respect to the attentional state of the listener. The constructed decoder is based on a maximum a posteriori (MAP) estimate of the state parameters via the Expectation Maximization (EM) algorithm. Using only the envelope of the two speech streams as covariates, the proposed decoder enables us to track the attentional state of the listener with a temporal resolution of the order of seconds, together with statistical confidence intervals. We evaluate the performance of the proposed model using numerical simulations and experimentally measured evoked MEG responses from the human brain. Our analysis reveals considerable performance gains provided by the state-space model in terms of temporal resolution, computational complexity and decoding accuracy. •Capturing MEG dynamics modulated by auditory attention via state-space modeling•Fast decoding of selective auditory attention using a novel application of EM theory•Achieving temporal resolution of the order of seconds in decoding the attention state•Providing Bayesian confidence intervals on the decoded attention state•Performance evaluation through simulated and experimental MEG data