The Journal of the Acoustical Society of America

Computational models of the middle ear are typically developed within the linear range of the middle ear's response, which corresponds to typical sounds in daily life. Understanding middle-ear biomechanics under high-intensity sounds is also important for assessing potential auditory damage under adverse conditions. In this study, a finite-element (FE) model of the human middle ear plus the external ear canal is presented, employing a first-order Ogden hyperelastic material formulation and time-domain nonlinear analysis to capture nonlinear deformation of soft tissues and the resulting middle-ear response. The model was analyzed using pure-tone pressure excitations applied at the ear canal entrance over the 80 Hz to 8 kHz frequency range and 110 to 180 dB sound pressure levels (SPL). The ear canal pressure near the tympanic membrane (TM) and full-field three-dimensional response of the middle ear structures were examined. Across the investigated frequency range, the simulated middle-ear motion exhibited a transition from predominantly piston-like behavior at low frequencies, up to 1 kHz, to increasingly intricate multi-modal and multi-directional vibration patterns at higher frequencies. Nonlinear behaviors became increasingly significant at high frequencies and high stimulus levels, including compressive/expansive pressure-displacement relationships, waveform distortions, and the emergence of non-harmonic spectral components. The model developed in this study provides a computational framework for studying nonlinear sound transmission through the middle ear and its implications for high-intensity acoustic exposure. The findings contribute to a better understanding of middle-ear mechanics under adverse conditions and support future investigations of acoustic trauma and hearing protection strategies.

Predicting when an event will occur is key to perceiving and evaluating dynamic sensory inputs, such as speech. The utility of temporal predictions hinges on perceptual timing precision (PTP), the sensitivity to deviations between actual and predicted sound onset times. Past work suggests that PTP is primarily determined by acoustic properties of target sounds, regardless of the acoustics of preceding sound cues. In contrast, in four experiments, we discover that PTP is best when context and target sound spectra and temporal envelopes match, with only minor effects of individual sound acoustics. This ability to utilize contextual continuity improved with musical aptitude. Theoretical modeling showed that spectrally tuned oscillatory entrainment to sound onsets and offsets is necessary to account for our results. In sum, the precision of temporal predictions is defined not by the acoustics of individual sounds but by the continuity of spectral and temporal content within a sound stream.

We investigated the hearing mechanism of Manduca sexta caterpillars to determine whether their behavioural responses to acoustic stimulation are caused by sound-induced substrate vibration or by direct detection of airborne sound. Behavioural response thresholds to direct substrate vibration and airborne sound were measured using pure-tone stimuli at 150 Hz and 2000 Hz. A frequency of 150 Hz was selected based on previous behavioural measurements showing the lowest sound detection thresholds between approximately 100 and 200 Hz, while 2000 Hz was selected based on laser Doppler vibrometry measurements that revealed a mechanical resonance near this frequency in a thoracic hair. At both test frequencies, the direct substrate vibration detection thresholds were more than 100 times greater, on average, than the sound-induced substrate vibrations measured at the behavioural sound detection thresholds. Thus, the behavioural responses to airborne sound cannot be explained by sound-induced substrate vibration. During airborne sound stimulation, caterpillars exhibited graded behavioural responses with increasing sound pressure level, progressing from freezing to twitching and finally to jump-startle. Together, these findings provide direct experimental evidence that the behavioural responses of M. sexta caterpillars to acoustic stimulation cannot be explained by sound-induced substrate vibration and instead support direct detection of airborne sound. Understanding how caterpillars respond to airborne sound may also provide biological inspiration for the development of bio-inspired acoustic sensors.

Semi-occluded vocal tract (SOVT) exercises are widely used in voice training and therapy, but their immediate electroglottographic (EGG) effects remain incompletely characterized. In this pilot methodological study, six female community singers completed a no-straw baseline recording and then 1-min straw-phonation exercises with four straws (Household metal, Metal VoiceStraw, plastic, and paper), each followed by a no-straw EGG recording. Thirty 1s recordings were analyzed with a standardized pipeline including contact quotient (CQ), waveform skewness, contact-phase rise/fall timing, temporal skewness (s_t), a normalized derivative asymmetry index (NDAI), average-cycle morphology, and EGG spectral descriptors. Relative to baseline, group-mean CQ was higher after all four straws (by 0.027-0.058), waveform skewness and NDAI were lower, and the EGG spectral centroid was lower (by 9.6-15.3%). Contact-phase timing asymmetry did not change consistently: s_t exceeded baseline after three straws and fell below it after one, and the rise/fall ratio decreased in three to five of six participants depending on the straw. Four participants ranked the Metal VoiceStraw first and two ranked paper first. Because every straw condition followed the baseline, warm-up and order effects cannot be separated from straw effects. These hypothesis-generating results identify CQ, waveform skewness, and EGG spectral centroid as candidate endpoints for a larger controlled study

Time-frequency detection of bird vocalizations is an important step toward turning weakly annotated field recordings into usable data for studying avian communication. Supervised detectors trained on human expert annotations scale poorly across species and recording conditions, so we introduce a teacher-student setup in which the teacher, a vision-language model, labels bounding boxes on spectrograms of citizen-scientist recordings, and those labels train a student, a self-supervised bioacoustic encoder. We find that the student generally exceeds both the teacher and supervised models trained on human annotations and performs strongly on held-out datasets for both time-frequency and onset-offset localization. YOLO detectors trained on our teacher labels match those trained on human annotations, suggesting that VLM labels can substitute for costly expert labeling.

Distinct auditory nerve (AN) fiber populations with varying thresholds and vulnerabilities to synaptopathy are observable in animal models, but standard non-invasive human measures lack the resolution to differentiate these groups of fibers. This fundamental limitation hinders the translation of findings in animals to human auditory pathologies. To overcome this, we developed a novel refractoriness-masking paradigm to non-invasively isolate threshold-specific AN fiber responses in humans. By presenting rapid click trains with cumulatively rising levels (45 to 105 dB peSPL) at a 250-microsecond inter-click interval, we sequentially masked lower-threshold fibers. Using a tympanic membrane electrode, we recorded compound action potentials (CAPs) in adults with normal hearing and isolated threshold-specific responses via waveform subtraction. Our results demonstrated that the isolated CAPs exhibited decreased latencies with increasing levels and a non-linear amplitude growth, plateauing at 90 dB peSPL before sharply increasing at 105 dB peSPL. This pattern reflects the sequential recruitment of AN fiber that were not effectively recruited by the preceding clicks. Validate conditions, in which the final two clicks had equal levels, produced substantially smaller CAPs, consistent with response suppression by refractoriness. Overall, this paradigm establishes a human measurement approach for characterizing level-threshold-dependent AN responses, providing a potential way to better investigate different AN pathologies, such as cochlear synaptopathy or auditory neuropathy.

Purpose: Stimulability testing involves the evaluation of multiple aspects of a patient's behavior, but the results are most frequently consolidated into a single domain (i.e., "stimulable"). The current study performs preliminary content, construct, concurrent, and predictive validation for the Multidimensional Stimulability Profile for Voice (MSP-V), a rating scale designed to measure multiple domains during stimulability testing (Degree of Change, Rapidity of Change, Generalization, Patient-rated Voice Quality, Patient-rated Voice Feel, and an overall rating of Stimulability Level). Methods: 78 patients with voice disorders (24 functional, 15 structural, 39 neurological) were administered the MSP-V by five speech-language pathologists (SLPs). Content validity was evaluated via the Content Validity Index (CVI) completed by nine voice-specialized SLPs. Construct validity was evaluated via exploratory factor analysis. Concurrent validity was assessed by determining whether MSP-V domains scaled in the expected direction compared to categorical stimulability ratings (e.g., "Highly stimulable" vs. "Stimulable"). Predictive validity was assessed by examining the relationship between MSP-V domains and voice disorder diagnosis, return to voice therapy, and patient-reported practice outside of therapy. Results: The CVI reached the critical value for clinician-rated and patient-rated domains. Two distinct factors were identified during EFA, one consisting of clinician-rated domains and the other of patient-rated domains. All MSP-V domains showed the expected directional relationships as categorical stimulability ratings. No domains differed significantly between disorder etiologies. The Rapidity of Change and Stimulability Level domains both significantly predicted return for voice therapy, where participants who rated lower on each domain were more likely to return to for voice therapy. No domains were predictive of patient-reported practice. Conclusions: The MSP-V is a clinically feasible tool with emerging content, construct, concurrent, and predictive validity that could be used to provide increased clarity and precision in stimulability testing documentation for research and practice.

The relationship between neural processes supporting perception, deviance processing, and post-perceptual task demands during auditory processing is not well understood. We used multitone masking and a no-report inattentional deafness paradigm to dissociate neural correlates of auditory perceptual awareness, deviance processing, and task demands. Evoked responses to physically identical target-tone pairs were recorded across three phases differing only in task instructions. In Phase A, human participants remained nave to (and thus unaware of) the target tones. In Phase B, participants were informed about (and thus aware of) the target tones. In Phase C, participants remained aware of the target tones and judged the duration of the silent gap between them, rendering them task-relevant. When participants were aware of them, target tones elicited an auditory awareness negativity and an increased positivity in the P3 window), with larger responses when target tones were additionally task-relevant. Furthermore, though largest in Phase C, deviant stimuli elicited a mismatch negativity across all task conditions, while a significant P3 to deviants was only observed in Phase C. Thus, early and late responses can be independently modulated by awareness, deviance, and task-relevance, indicating that they do not necessarily rely on shared neural mechanisms despite overlapping spatiotemporal profiles.

Bilateral cochlear implantation (BCI) can improve spatial hearing in adults with bilateral hearing loss, but the early development of spatial hearing following sequential BCI remains poorly understood. This prospective longitudinal study investigated changes in spatial hearing experience in 23 adults undergoing sequential BCI, assessed before and at ~1, 3, and 8 months after bilateral implantation under two conditions: spontaneous and restrained head movements. Using a motion-tracking system combined with virtual reality, sound localization was assessed through absolute and signed errors, along with self-reported localization confidence and listening effort. Subjective spatial hearing was also assessed using the Speech, Spatial and Qualities of Hearing Scale (SSQ). Localization performance improved by 1 month after bilateral implantation, as evidenced by a reduction of absolute error and in the bias toward the first-implanted ear. Participants reported greater localization confidence and lower listening effort at 1 month, although these changes were not consistently maintained at later follow-ups. SSQ scores showed improved spatial hearing at 1 month, speech perception at 3 months, and sound clarity and discriminability at 8 months. Compared with head-restrained condition, accuracy and confidence improved when spontaneous head rotations were allowed. The temporal trajectories of localization bias, confidence, and effort were modulated by the duration of experience with the first CI. These findings indicate that changes in spatial hearing can emerge soon after second CI activation. They further highlight the contribution of spontaneous head movements and previous CI experience, and support the use of complementary measures to monitor the early development of bilateral listening.

Neural tracking of speech is a well-established phenomenon in neuroscience. However, for speech signals with a fixed structure, significant correlations between speech envelopes and neurophysiological representations occur even for unheard sentences. We exploit a structured speech-in-noise matrix hearing test (Oldenburger Sentence Test, OLSA) to systematically quantify the relationship between acoustic sentence similarity and neural tracking. Simultaneous magnetoencephalography (MEG) and 76-channel electroencephalography (EEG) data, including 16 channels positioned directly around the ears (ear-EEG), were recorded from 21 young adults with normal hearing during the presentation of clean-speech audiobooks and OLSA sentences at six signal-to-noise ratios. A linear decoder trained on audiobooks reconstructed OLSA sentence envelopes. Reconstruction accuracies were compared using a linear mixed model across heard (matched) and unheard (mismatched) sentences of varying acoustic similarity. Significant reconstruction accuracies were achieved across MEG, EEG, and ear-EEG for both matched and mismatched sentences. For mismatched sentences, these accuracies gradually increased with their acoustic similarity to the heard speech data. The high similarity between sentences, which is especially prominent in matrix tests, can cause significant spurious tracking for mismatched stimuli. This effect can reach levels comparable to those of matched sentences and can be mistaken for true neural tracking. Robust neural tracking across modalities further supported the established viability of ear-EEG compared to whole-head systems.

1. Re-identifying individual animals (re-ID) from their calls with machine learning (ML) could extend individual-based monitoring to species and areas hard to survey by capture and marking, and published studies often report re-ID accuracy above 90%. Two methodological practices can inflate these values: (1) attributing calls to individuals by territory, nest, tag or station, tying each animal to its own recording conditions, and (2) randomly splitting individual clips into training and test sets, allowing recording context to be shared between them. Each effect has so far been documented only in single-species studies. 2. We implement four diagnostics of how methodological choices affect re-ID accuracy: (1) splitting clips by recording session; (2) background tests using ambient recordings without the animal; (3) within-recording permutation and paired-clip tests where animals share recordings; and (4) open-set tests with thresholds set on other individuals. We applied them to 13 public datasets from nine species (eight birds, one bat), using BirdNET, 19 other neural networks trained on animal or general sound and 16 trained on human speech, each with one classifier. 3. Under session splits, BirdNET re-ID accuracy ranged from 0.16 to 0.97. Within one year, ambient sound alone carried substantial identity information for territorial chiffchaffs (BirdNET balanced accuracy 0.71, against 0.79 for song); background identification exceeded chance for 35 of 38 embeddings and controls. Count-matched random clip splits raised mean accuracy by 0.20 to 0.51 in 18 comparisons across three species. Where several animals shared recordings, only one of three datasets showed clearly that re-ID rested on the animal rather than the recording. With unfamiliar animals present and thresholds calibrated to target 10% stranger acceptance, across 36 neural networks and two scoring methods on wild session-split datasets, the highest median share of known birds' calls accepted and correctly named was 46%, using nearest-clip scoring with four enrolled birds; test-stranger acceptance could exceed the target. No neural network beat BirdNET on every dataset, and network rankings depended on layer choice. 4. The diagnostics need no new models and apply wherever a dataset holds what they require: session labels, background recordings, shared recordings or enough individuals. Re-ID accuracy reported without them can substantially overstate what acoustic re-ID achieves, and should not be used to plan population monitoring until they are applied.

Early detection of invasive species is critical to efficiently managing biological invasions. Passive acoustic monitoring combined with deep learning has become an effective tool for identifying invasive anurans from their species-specific mating calls. In practice, however, these systems still depend on manually retrieving recordings and running inference on a dedicated workstation, which delays detection at invasion fronts, especially on remote islands where frequent travel is impractical. Here, we present a lightweight convolutional neural network for detecting Rhinella marina and Polypedates leucomystax that can be used with low-power microcontrollers, enabling continuous, real-time monitoring of these invasive species. Our model achieved a mean invasive-species F1 of 0.87 with precision higher than 0.88 for both species, comparable to the performance of substantially larger models at a fraction of the model size. Cross-domain evaluation confirmed reliable detection on two out-of-training locations: Iriomote Island, Japan, and on an independent dataset from Australia, demonstrating generalization across recording conditions and biogeographic contexts, albeit with local threshold calibration for P. leucomystax at Iriomote. Our results demonstrate that effective acoustic detection of invasive species is feasible on self-contained, battery-powered devices, removing the dependence on server infrastructure and enabling autonomous early warning systems in remote protected areas.

Protecting deep-sea sessile communities as Marine Protected Areas (MPAs) requires accurate estimates of their spatial distribution and abundance. Existing computer-vision detection methods require careful calibration to avoid counting errors; multi-object tracking (MOT) theoretically offers a more robust alternative. In practice, however, poor lighting and turbidity limit the efficacy of purely kinematic MOT algorithms in deep-sea environments. At the same time, the intra-species visual homogeneity typical of deep-sea scenes means traditional per-frame appearance features are often too weak to maintain stable identities in deep-association MOT algorithms. In this paper we introduce JEPA-SORT, an MOT algorithm that injects V-JEPA 2.1 embeddings into detection-to-track association, to show that video world-model embeddings - which capture not only physical appearance but spatiotemporal trajectory across viewpoints - can help maintain stable identities on visually homogeneous specimens. We provide a benchmark of twelve ROV transects surveying deep-sea sessile communities and evaluate JEPA-SORT against standard MOT algorithms across a range of simulated optical environments. We also propose the Biodensity Utility Metric (BDU), which measures tracker performance in the context of downstream ecological modeling. JEPA-SORT outperforms all benchmarked trackers on IDF1, MOTA, and BDU and is competitive on specimen-count accuracy. Ablations indicate its advantage stems primarily from the discriminative power of world-model embeddings on visually homogeneous footage.

The pure-tone audiogram remains the cornerstone of hearing assessment, yet it captures only part of the functional consequences of cochlear damage. Most phenotyping schemes rely solely on the audiogram, which cannot detect hidden suprathreshold dysfunction when thresholds are normal. How best to represent audiological data - as discrete phenotypes or as a continuous space - remains unresolved. Using a retrospective database from the Copenhagen Hearing and Balance Centre, Rigshospitalet (Denmark; 1995-2022; 251986 ear-session units from 81883 adults with normal or sensorineural audiograms), we compared linear (PCA) and non-linear (UMAP) embeddings of the audiogram alone and with word recognition in quiet (WRSmax-Q). Here we show that a linear embedding suffices for the audiogram, but a non-linear one becomes advantageous once suprathreshold measures are added. PCA compressed the audiogram onto two interpretable axes, overall level and slope, explaining 91.5% of the variance, with ears forming a continuous cloud that published reference schemes tiled into overlapping regions. For the audiogram alone, UMAP recovered the same organisation. WRSmax-Q varied widely among ears with similar audiograms; adding it left the PCA unchanged but reorganised the UMAP. A synthetic experiment illustrated this: ears with identical audiograms but different WRSmax-Q separated under UMAP, not PCA. Combining thresholds with WRSmax-Q, UMAP yielded a continuous representation - a prototype Hearing Loss Map - where each ear's position reflects threshold and suprathreshold measures. Representing hearing loss as a location in a continuous space rather than as discrete categories may better reflect its graded nature, with clinical relevance for individual patients.

Standard hearing tests miss cochlear neural degeneration (CND), the loss of synapses between inner hair cells and auditory nerve fibers that may underlie unexplained hearing difficulty in roughly one in ten adults. Envelope following responses (EFRs) can index this neural loss noninvasively, but conventional protocols are too slow for clinical use. We tested a dynamic amplitude-modulated (dAM) EFR that sweeps the full modulation spectrum in a single brief stimulus. In Mongolian gerbils with histologically verified CND, dAM EFRs revealed selective deficits at fast modulation rates without threshold elevation. A machine-learning classifier distinguished young from middle-aged animals with high accuracy; additionally, its most informative feature, power near 400-500 Hz, tracked synapse counts, indicating a readout of cochlear neural health rather than age. Applied without retraining to 56 human listeners, the gerbil-trained classifier separated age groups above chance. dAM EFRs thus offer a rapid, objective and clinically viable marker of CND.

Many individuals experience problems that are undetected by standard hearing tests, such as persistent tinnitus and difficulties understanding speech in background noise. Here, we report evidence of altered neural gain in young adults with self-reported listening problems despite exceptional hearing thresholds. Individuals reporting persistent tinnitus (the daily percept of sound in the absence of an external source) exhibited elevated thresholds of the middle ear muscle reflex, suggestive of altered gain in brainstem reflexive circuits, and reduced cortical responses to auditory spatial cues conveyed in the temporal fine structure of low-frequency sounds. Moreover, in both groups, greater self-reported speech-in-noise difficulties were associated with higher magnitude cortical responses, particularly at the highest stimulus intensity, consistent with elevated neural gain. Using a conceptual model, we demonstrate how damage to high-threshold auditory nerve fibres innervating cochlear nuclei can account for reduced activity of binaural neurons sensitive to the temporal fine structure of low-frequency sounds, generating a compensatory increase in neural gain in higher brain centres that impacts listening in complex environments.

Vocal communication depends on the biomechanical integrity of the vocal folds, yet how impairment alters vocal behavior remains poorly understood. We used the California mouse (Peromyscus californicus), a highly vocal species that produces songs through airflow-induced vocal fold vibration, to experimentally examine how defined vocal fold injury alters vocal production. Longitudinal recordings were combined with histological assessment to track changes in vocal function after impairment. Injury produced acute edema of the lamina propria and thyroarytenoid muscle. Despite stable levels of vocal activity, the vocal phenotype shifted characteristically following injury. Thus, vocal behavior can persist despite substantial alteration of the biomechanical tissues underlying sound production, demonstrating that maintenance of behavioral output does not necessarily indicate preservation of the physiological system that generates it. More broadly, these findings establish an experimental link between vocal fold injury and vocal phenotype and provide a framework for understanding how tissue damage and remodeling can reshape acoustic communication even when vocal behavior is maintained. Because repeated vocalization can impose substantial mechanical demands on vocal folds, this framework may have broad relevance for understanding how vocal use, injury, and tissue remodeling interact to shape vocal communication.

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

Objective: Kabuki syndrome type 2 (KS2) is a rare X-linked disorder caused by pathogenic variants in KDM6A and is frequently associated with hearing impairment. Because auditory phenotypes include sensorineural, conductive, and mixed hearing loss, we examined whether Kdm6a deficiency directly impairs cochlear morphology or auditory function in mice. Methods: We generated Nestin-Cre; Kdm6a conditional mutants and a germline-derived global Kdm6a deletion model using Sox2-Cre-mediated germline recombination. Auditory function and cochlear morphology were assessed by auditory brainstem responses (ABRs), distortion product otoacoustic emissions (DPOAEs), and histological analyses. Results: Whole-body hemizygous or homozygous Kdm6a deletion resulted in embryonic lethality. Analyses therefore focused on Nestin-Cre; Kdm6aflox/Y males and females carrying a germline-derived heterozygous Kdm6a deletion. In Nestin-Cre mutants, ABR thresholds, wave I amplitudes and latencies, and DPOAE measures at 8, 16, and 32 kHz were comparable with littermate controls at 4 and 8 weeks. In heterozygous deletion females, ABR thresholds, wave I amplitudes and latencies at 4-64 kHz and DPOAE measures at 8, 16, and 32 kHz were comparable with male and female wild-type controls. No overt cochlear pathology was observed in either model. Conclusion: Kdm6a deficiency in the viable mouse models examined did not produce overt cochlear pathology or baseline auditory dysfunction. These findings suggest that partial Kdm6a loss alone is insufficient to produce a robust primary cochlear phenotype in young mice and highlight the potential contributions of non-cochlear, developmental, central, and later-onset mechanisms to hearing impairment in KS2. Clinically, these findings support careful evaluation for potentially treatable middle-ear disease.

Aging has profound effects on sensory systems, such that older adults can report problems in hearing despite having normal audiometric thresholds. While it is known that older listeners have poorer temporal processing than younger listeners, there is limited understanding of how these deficits interact with effects of sound level, in particular when they are above conversational level (e.g., > 65 dB SPL). Here we wanted to investigate the link between temporal processing and sound level as a function of age in normal-hearing listeners. Using a forward-masking paradigm, auditory brainstem responses (ABRs) and behavioral detection thresholds were measured at different masker levels. It was found that with increasing age and masker sound level, the Wave V amplitudes of older listeners decreased and directly correlated with behavioral detection thresholds of the probe stimulus in the forward-masking paradigm. These results elucidate the fundamental effects of aging on temporal processing and its interactions with sound level in the central auditory system. The results of this study have clinical implications for the concept of normal hearing in the aging population and how older listeners may struggle in different listening environments that are not captured by simple audiometry. Further delineating the important effects of aging will pave the way for earlier clinical diagnosis of hearing disorders and personalized treatments for different forms of age-related hearing loss.