Ten years of the stochastic resonance model of tinnitus: from phantom perception to adaptive sensory optimization

1 Introduction

Tinnitus affects approximately 10–15% of the population and constitutes one of the most prevalent examples of a sensory phantom percept (Henry et al., 2020). Despite extensive experimental and theoretical work, a unified mechanistic account of tinnitus generation has not yet emerged. A large body of research links tinnitus to peripheral hearing loss and subsequent changes in central auditory processing, most prominently increased spontaneous firing rates and neural hyperactivity along the auditory pathway (Kaltenbach and McCASLIN, 1996; Ma et al., 2006; Norena and Eggermont, 2003; Ropp et al., 2014). Classical explanatory frameworks, including central gain and homeostatic plasticity models, interpret these changes as compensatory responses to reduced sensory input that become maladaptive over time (Auerbach et al., 2014; Roberts and Salvi, 2019; Yang et al., 2011). While such models successfully account for several physiological and behavioral observations, neuronal hyperactivity is typically regarded as an epiphenomenon or pathological side effect of compensation rather than as a functional component of auditory processing.

In 2016, the stochastic resonance (SR) model introduced an alternative conceptual perspective by proposing that increased spontaneous activity may serve a functional role in sensory processing. In this framework, internally generated noise is adaptively regulated to improve signal detection and maintain optimal information transmission under conditions of degraded input (Krauss et al., 2016). From this viewpoint, tinnitus-related hyperactivity does not primarily reflect dysfunctional amplification but may instead arise as a consequence of an adaptive neural mechanism operating near sensory detection thresholds. In the model, tinnitus is therefore interpreted not as the primary failure of the auditory system, but as a perceptual side effect of a compensatory process aimed at restoring sensitivity following hearing loss. In this sense, tinnitus-related hyperactivity is interpreted as a functional consequence of adaptive sensory optimization aimed at maintaining information transmission under degraded input conditions.

The SR framework developed in relation to three complementary classes of tinnitus models. Homeostatic-plasticity and central-gain accounts describe compensatory changes in excitability following reduced peripheral input, whereas predictive-coding accounts address how uncertain sensory evidence and prior expectations contribute to conscious phantom perception. The SR model adds a proposed mechanism at the level of signal detection: adaptively regulated internal noise may facilitate the transmission of weak inputs while producing increased spontaneous activity as a side effect. Phenomena that can emerge or change on short timescales, including somatic modulation, tinnitus after temporary deprivation, and the Zwicker tone, motivated this emphasis on rapid signal-detection processes. The chronological account below introduces detailed comparisons where they became relevant to the development of the framework, thereby distinguishing their explanatory levels without treating them as mutually exclusive.

The present article provides a chronological review of the development of the stochastic resonance model over the past decade. We trace its conceptual origins, summarize key theoretical and computational advances, and discuss how its predictions relate to empirical findings in auditory neuroscience. By situating the SR framework within the broader landscape of tinnitus models, this review aims to clarify both its explanatory scope and its limitations, and to outline open questions for future research.

2 Methodology and scope of the review

This article is a narrative review that provides a chronological overview of the development of the stochastic resonance model of tinnitus over the past decade. Rather than performing a formal systematic review or meta-analysis, our aim is to synthesize the conceptual development of the model, its theoretical refinements, computational implementations, experimental evidence, and clinical applications. The literature considered for this review was identified through the authors' continuous engagement with the field, complemented by targeted searches in PubMed and Google Scholar using combinations of keywords including tinnitus, stochastic resonance, internal noise, phantom perception, central gain, homeostatic plasticity, and predictive coding. Particular emphasis was placed on peer-reviewed original research articles directly related to the stochastic resonance framework, together with influential competing models that provide the broader scientific context. As the primary purpose of this review is to document the historical development of one specific theoretical framework, no formal inclusion/exclusion criteria or quantitative evidence synthesis were applied.

3 History of the stochastic resonance model of tinnitus

The stochastic resonance model of tinnitus did not emerge in isolation but developed at the intersection of information theory, auditory neuroscience, and the broader study of noise-enhanced signal processing in biological systems. Over the past decade, the framework evolved from an initial hypothesis explaining tinnitus-related hyperactivity after hearing loss into a broader theory of adaptive sensory processing, phantom perception, and clinical intervention. The following sections trace this development chronologically, highlighting key conceptual advances, empirical findings, and theoretical extensions. A chronological overview of the development of the SR model is provided in Figure 1.

3.1 Before 2016: stochastic resonance, noise and information theory

Prior to its application to tinnitus, stochastic resonance (SR) had been established as a general phenomenon in nonlinear dynamical systems (Benzi et al., 1981; Gammaitoni et al., 1998). Initially described in physical contexts, SR demonstrated that the addition of noise to a nonlinear detector can improve signal transmission when inputs fall below detection threshold. Rather than degrading performance, noise under appropriate conditions can enhance the detectability of weak signals by enabling threshold crossings that would otherwise not occur. This was, for example, demonstrated by adding mild noise to acoustic stimuli below the detection threshold (Zeng et al., 2000). Subsequent experimental and theoretical work extended this principle to biological systems, showing that noise-enhanced signal detection can arise in sensory receptors and neural populations, thereby establishing SR as a biologically plausible mechanism rather than a purely physical curiosity (Itzcovich et al., 2017; Moss et al., 2004; Manjarrez et al., 2003). These developments provided the theoretical foundation for later applications of SR to auditory processing, tinnitus, and phantom perception.

3.2 2016: the first formulation of the stochastic resonance model of tinnitus

The stochastic resonance model of tinnitus was first formally introduced in 2016 by Krauss and colleagues, who proposed that tinnitus-related neuronal hyperactivity may arise from an adaptive increase of internally generated noise following hearing loss (Krauss et al., 2016). The model was motivated by the observation that reduced peripheral input decreases the detectability of weak sensory signals, while increased spontaneous activity is consistently observed at multiple levels of the auditory pathway in tinnitus (Kaltenbach and McCASLIN, 1996; Ma et al., 2006; Norena and Eggermont, 2003; Ropp et al., 2014). Rather than interpreting this hyperactivity as purely maladaptive, the model suggested that it may reflect a compensatory mechanism aimed at preserving information transmission under degraded input conditions.

3.2.1 Conceptual innovation

The central conceptual contribution of the model consisted of two closely related shifts in interpretation. First, spontaneous neural activity was reinterpreted functionally. Increased spontaneous firing rates were not assumed to represent pathological overexcitation, but were instead proposed to constitute internally generated noise required for optimal signal detection in nonlinear sensory systems. Within this framework, neuronal hyperactivity emerges as a necessary by-product of an adaptive process operating near detection threshold (Krauss et al., 2016).

Second, the model proposed that the auditory system regulates internal noise levels so as to maximize information transmission between sensory input and neural response. This information-theoretic perspective later motivated the development of biologically plausible adaptive stochastic resonance mechanisms based on output autocorrelation (Krauss et al., 2017).

This interpretation differs fundamentally from homeostatic plasticity and central gain models (Auerbach et al., 2014; Roberts and Salvi, 2019; Yang et al., 2011). Whereas these frameworks assume compensatory gain changes acting on already detected signals, the stochastic resonance model operates at the level of signal detection itself. By increasing internal noise, subthreshold inputs can cross neuronal thresholds, thereby improving detectability before amplification mechanisms become relevant.

3.2.2 Computational implementation

The original model was formulated as a phenomenological feedback system consisting of three functional components: a sensory detector representing the cochlear or early auditory stage, an information detector computing the autocorrelation of neural responses, and a noise generator whose output was regulated through feedback (Krauss et al., 2016). Reduced sensory input following hearing loss decreased output autocorrelation, which in turn increased internally generated noise until information transmission was partially restored. Computational simulations demonstrated that this adaptive noise regulation improves detection performance under elevated thresholds while simultaneously increasing spontaneous activity within the system (Krauss et al., 2016). Within the proposed framework, this increased activity represents the neural correlate of tinnitus, emerging as a consequence of adaptive sensory optimization rather than as a primary dysfunction.

3.2.3 Empirical consistency

In addition to its theoretical and computational formulation, the 2016 study analyzed audiometric data from over 39,000 patients (Krauss et al., 2016; Gollnast et al., 2017). Tinnitus patients exhibited improved low-frequency hearing thresholds compared to patients without tinnitus, which is consistent with threshold compensation mediated by stochastic resonance (Gollnast et al., 2017). Subsequent work focused on biological plausibility by linking the functional components of the stochastic resonance framework to auditory structures known to be capable of implementing adaptive noise regulation. Rather than assigning a single anatomical location, stochastic resonance was interpreted as a functional principle operating at multiple early processing stages (Krauss et al., 2016). Particular attention was given to the lateral olivocochlear efferent system, which influences auditory nerve activity as a descending pathway, and to the dorsal cochlear nucleus, whose circuitry supports the processing and integration of temporal correlations and auditory and somatosensory inputs (Krauss et al., 2016). The dorsal cochlear nucleus provides a mechanistic link to somatic tinnitus and exhibits rapid activity changes after acoustic trauma, which is consistent with adaptive processes operating on short timescales (Krauss et al., 2016; Shore, 2005; Shore and Zhou, 2006).

3.2.4 Integration with hidden hearing loss and synaptopathy

The SR framework also naturally accounts for tinnitus in individuals with clinically normal audiograms (Schaette and McAlpine, 2011). Hidden hearing loss and cochlear synaptopathy can reduce the fidelity of neural information transmission without substantially affecting audiometric thresholds. Because adaptive stochastic resonance responds to diminished information transfer rather than absolute hearing thresholds, the model predicts compensatory upregulation of internal neural noise even in the absence of measurable hearing loss. This provides a mechanistic explanation for the frequent occurrence of tinnitus in patients whose standard audiometric measures remain within normal limits (Schilling et al., 2020c).

3.2.5 Support from ear-plugging studies

Additional support for the stochastic resonance framework comes from ear-plugging studies in humans. In these experiments, temporary unilateral auditory deprivation is induced by inserting an ear plug into one ear, thereby reducing auditory input without causing structural damage to the auditory system. Remarkably, healthy participants frequently report the emergence of transient tinnitus-like phantom percepts during or shortly after the deprivation period (Schaette et al., 2012; Fournier et al., 2014). These findings are difficult to explain solely by permanent cochlear pathology because no lasting peripheral damage is present. Instead, they suggest that reduced auditory input alone can trigger neural adaptations leading to phantom sound perception. Within the stochastic resonance framework, ear plugging decreases information transmission from the affected ear, resulting in adaptive upregulation of internally generated neural noise to maintain sensitivity. The transient tinnitus percept is therefore interpreted as a direct consequence of this compensatory process. Ear-plugging studies thus provide an important experimental bridge between chronic tinnitus associated with hearing loss and transient phantom percepts such as the Zwicker tone, demonstrating that reduced sensory input can be sufficient to induce tinnitus-like sensations even in otherwise healthy auditory systems.

3.2.6 Comparison with competing models

Over the past decade, competing models of tinnitus have differed primarily in their proposed mechanisms and interpretations of neuronal hyperactivity. Homeostatic plasticity and central gain accounts attribute increased activity to compensatory amplification following reduced peripheral input. These accounts view tinnitus as a maladaptive byproduct of this upregulation (Auerbach et al., 2014; Roberts and Salvi, 2019; Yang et al., 2011). In contrast, predictive coding frameworks emphasize altered perceptual inference. These frameworks propose that unreliable sensory input shifts the balance toward internally generated predictions that become consciously perceived (Sedley et al., 2016; Hullfish et al., 2019; De Ridder et al., 2024). The stochastic resonance model, in contrast, interprets hyperactivity as the functional correlate of adaptive internal noise regulation that enhances signal detectability under degraded input conditions (Krauss et al., 2016). According to this model, tinnitus emerges as an unavoidable side effect of this optimization process rather than as mere pathological overexcitation (Krauss et al., 2016).

3.3 2017–2022: from a tinnitus model to a general theory of adaptive sensory processing and phantom perception

Evidence for the biological plausibility of the SR framework was strengthened in 2017 by the introduction of an adaptive stochastic resonance mechanism capable of regulating internal noise levels without requiring knowledge of the sensory input signal. Instead, the model demonstrated that output autocorrelation can serve as a biologically plausible proxy for information transmission and can be used to continuously adjust noise levels through a feedback loop, thereby maximizing signal detectability under unknown and variable input conditions. Importantly, the study showed that adaptive stochastic resonance is not restricted to Gaussian noise, periodic signals, or hard detection thresholds, as often assumed, but remains effective for non-Gaussian noise distributions, aperiodic signals, and biologically more realistic soft-threshold systems, thereby considerably broadening its potential relevance for neural information processing (Krauss et al., 2017).

Building on this general framework, subsequent work extended the SR model beyond tinnitus-specific mechanisms and toward a broader theory of adaptive sensory processing and phantom perception (Krauss et al., 2018a,b; Schilling et al., 2021). The central idea was that internally generated neural noise constitutes a functional resource that can be dynamically regulated to maintain information transmission under degraded sensory conditions. Within this perspective, tinnitus and related phantom percepts emerge as side effects of an otherwise beneficial optimization process that improves signal detectability and sensory performance. This broader interpretation culminated in the concept of cross-modal stochastic resonance, which proposed that internally generated or cross-modal neural activity can serve as a functional source of noise that enhances sensory processing across modalities (Krauss et al., 2018b).

It was proposed that phantom percepts arise when adaptive sensory circuits attempt to maintain information transmission under persistently degraded input conditions. In this context, the model provided a mechanistic foundation for Fan Gang Zeng's 2013 central noise model (Zeng, 2013). Thus, psychophysical findings have shown that central gain alone cannot explain tinnitus because tinnitus is not reliably associated with recruitment or hyperacusis or simple amplification of auditory stimuli (Zeng, 2013), and it often occurs without exaggerated sound-evoked responses. While the central noise model suggested a role for internally generated neural noise, it did not specify its origin. The stochastic resonance framework addressed this gap by identifying adaptive internal noise regulation as a concrete, circuit-level mechanism that enhances signal detection while generating phantom percepts as a byproduct (Schilling and Krauss, 2022; Krauss and Tziridis, 2021; Zeng, 2013, 2020).

In addition, the model evolved from a single-channel formulation to a multichannel perspective, in which frequency-specific pathways operate as partially independent adaptive detectors (Schilling et al., 2021). Local regulation of internal noise in frequency regions affected by input degradation provides a mechanistic explanation for the frequency-specific characteristics of tinnitus and its clustering in regions associated with hearing loss. Subsequent work has related the framework to empirical findings, such as improved tone detection in external noise, transient phantom perceptions after sensory deprivation, somatosensory modulation of tinnitus, and the co-occurrence of tinnitus and hyperacusis (Shore et al., 2007; Cederroth et al., 2020).

In 2021, evidence from an animal model showed that simulated transient hearing loss induced by long-term notched-noise exposure improved frequency-specific auditory sensitivity while simultaneously eliciting tinnitus-like phantom percepts, supporting the hypothesis that adaptive stochastic resonance enhances signal detection at the cost of internally generated phantom sounds (Krauss and Tziridis, 2021).

A particularly important computational milestone concerned complex auditory processing beyond simple signal detection. As expected, in deep neural-network simulations, simulated hearing loss itself did not improve speech recognition but rather decreased accuracy. Whereas, adding an appropriate level of intrinsic noise partially restored performance relative to the impaired condition without added noise (Schilling et al., 2020a, 2022). However, the effect did not constitute evidence for better-than-normal hearing. This computational result is therefore not inconsistent with evidence that cochlear neural degeneration can impair hearing in background noise and increase cortical internal noise (Resnik and Polley, 2021). Rather, the comparison highlights a central constraint of the SR hypothesis: a benefit is predicted only when noise is introduced at an appropriate processing stage and adaptively maintained near an optimum, whereas insufficient, excessive, or dysregulated internal noise should degrade neural coding and perceptual performance.

The beneficial effects of stochastic resonance are expected to depend on both processing stage and operating regime. Near nonlinear detection thresholds, appropriately scaled internal noise can increase the probability that informative fluctuations elicit a response. More generally, stochastic resonance and suprathreshold stochastic resonance predict that non-zero noise levels can improve information transmission in nonlinear systems under specific conditions, provided that noise remains appropriately regulated (Gammaitoni et al., 1998; Stocks, 2000; McDonnell et al., 2006; Liu et al., 2021). Conversely, excessive, insufficient, or dysregulated internal variability is expected to degrade temporal, spectral, or population codes. Adaptive regulation must therefore continuously adjust internal noise to the current sensory context rather than maintaining uniformly elevated variability. The consequences for suprathreshold perception, including speech recognition in background noise, loudness growth, and hyperacusis, remain incompletely specified and constitute important tests of the scope and limits of the SR framework.

Such functional benefits may help explain why some tinnitus patients exhibit less cognitive decline than expected given their degree of hearing loss (Hamza and Zeng, 2021; Schilling and Krauss, 2022).

Taken together, these developments support the idea that tinnitus is an emergent consequence of adaptive sensory optimization processes that aim to maintain information transmission under adverse sensory conditions.

3.4 2023–now: toward a unified theory of tinnitus development

By 2023, it had become clear that no single model could account for the full range of tinnitus phenomena. Conceptual parallels had already been noted between the stochastic resonance framework and predictive or efficient coding theories, as both assume that sensory systems actively compensate for reduced or unreliable input to preserve stable representations (Sedley et al., 2016; Hullfish et al., 2019; De Ridder et al., 2024). However, these mechanisms operate at different explanatory levels. Stochastic resonance acts at the level of signal detection, modulating internal variability, and explaining the induction of tinnitus-related hyperactivity, particularly at the level of the dorsal cochlear nucleus (Schilling et al., 2023). Homeostatic plasticity and central gain mechanisms along the auditory pathway can subsequently amplify this hyperactivity, providing a link to the strength of recruitment and the frequent association between tinnitus and hyperacusis (Schilling et al., 2023). In contrast, predictive coding or Bayesian brain accounts address how such internally generated activity becomes a conscious perception. Within this unified perspective, stochastic resonance-induced hyperactivity may initially be misinterpreted as a real external tone, leading to acute tinnitus (Schilling et al., 2023). With ongoing internally generated noise, higher-level predictions gradually update, stabilizing the phantom perception and contributing to chronic tinnitus. In response to these complementary strengths, a consortium was formed in 2023 to integrate stochastic resonance, gain-based plasticity, and predictive coding into a generalized framework of tinnitus development. This framework connects the induction, amplification, and conscious perception of tinnitus within a single, coherent model (Schilling et al., 2023). The resulting integrated framework is illustrated in Figure 2.

4 Limitations and open questions

Despite the substantial conceptual and empirical progress achieved over the past decade, several important limitations and open questions remain.

The evidence reviewed here spans distinct levels of analysis and should not be interpreted as uniformly demonstrating the SR mechanism in humans. Computational studies establish proof of principle by showing that appropriately scaled intrinsic noise can improve signal detection or speech classification after simulated hearing loss, whereas animal experiments, audiometric associations, and early clinical studies test different and only partially overlapping predictions of the framework. In particular, the speech-recognition results reported by Schilling et al. (2020a, 2022) were obtained in artificial neural-network models and have not yet been demonstrated in human participants. Accordingly, these findings support the computational plausibility of SR-based compensation but do not by themselves establish that the human auditory system implements the proposed mechanism.

Several publications discussed in this chronological account are conceptual, perspective, or synthesis articles rather than reports of new empirical data. Their contribution lies in refining the SR hypothesis, relating it to central gain and predictive-coding accounts, and deriving testable predictions. They should therefore be distinguished from original computational, animal, observational, and clinical studies. In the following sections, conceptual publications are cited as developments of the theoretical framework, whereas claims regarding empirical support are based only on studies reporting original computational, animal, observational, or clinical data.

While hidden hearing loss and cochlear synaptopathy provide a plausible substrate for SR-mediated compensation, the relationship between cochlear pathology, internally generated neural noise, and perceptual outcome is likely more complex than a simple increase in internal variability. An important constraint is provided by Resnik and Polley (2021), who showed that cochlear neural degeneration was associated with increased internal noise in auditory cortex and impaired hearing in background noise (Resnik and Polley, 2021). This finding indicates that the functional consequences of increased internal variability depend on where and how such variability arises. Within the SR framework, this observation could be reconciled by distinguishing between appropriately scaled and regulated noise at nonlinear detection stages and excessive, downstream, or insufficiently regulated cortical variability. Whether the cortical changes observed after cochlear neural degeneration represent a maladaptive consequence of compensatory processes, the propagation of an earlier SR-like mechanism, or a distinct phenomenon remains an open question that requires direct experimental investigation across different levels of the auditory system.

The proposed relevance of stochastic resonance beyond audition concerns a general signal-processing principle and does not imply that all sensory systems should generate tinnitus-like percepts. Noise can facilitate threshold crossings in diverse nonlinear sensory circuits without necessarily reaching conscious awareness. A phantom percept would additionally require modality-specific conditions, including the organization of spontaneous activity, the persistence and localization of sensory deprivation, and the interpretation of internally generated activity by higher-level perceptual systems. Phantom sensations and deprivation-related hallucinations occur in other modalities, but their phenomenology and mechanisms are not equivalent to tinnitus; cross-modal SR should therefore currently be regarded as a hypothesis about adaptive information processing rather than a universal account of phantom perception.

Sound-based interventions, including tinnitus maskers and broader forms of sound therapy, were established well before the formulation of the SR model. The SR-derived approach should therefore not be understood as introducing sound therapy itself, but as providing a specific mechanistic rationale for using spectrally targeted, near-threshold noise in frequency regions associated with hearing loss or the tinnitus percept (Schilling et al., 2020b; Tziridis et al., 2022, 2025). The available studies have assessed the feasibility and potential effects of this parameterization, but they have not demonstrated superiority over conventional broadband maskers or other sound-therapy protocols. Direct, adequately powered comparisons with active control conditions are required to determine whether the SR-based parameter selection provides an incremental clinical benefit.

5 The future of the SR model and tinnitus research

Ten years after its introduction, the stochastic resonance framework has evolved from a theoretical proposal into a unifying perspective on tinnitus development, connecting auditory physiology, computational neuroscience, and adaptive information processing. By interpreting tinnitus-related hyperactivity as a consequence of sensory optimization rather than pathological overexcitation, the model shifts the focus from malfunction to adaptation. It also reframes phantom perception as a potential byproduct of mechanisms that normally preserve sensitivity under degraded input conditions.

The next decisive step is empirical verification at the neural level. Demonstrating adaptive internal noise regulation directly within the auditory system would provide critical support for the framework. Such evidence would elevate the model from theoretical plausibility to mechanistic validation. In parallel, rigorous quantitative testing is required. The stochastic resonance framework makes concrete, falsifiable predictions, such as improved detectability under specific noise regimes and the coexistence of enhanced sensitivity with phantom perceptions. Experimental paradigms capable of distinguishing these dynamics from explanations based solely on gain changes or plasticity are essential.

A central empirical challenge is to identify where, when, and under which conditions internally generated neural variability supports auditory processing rather than degrading it. Future experiments should quantify internal variability across multiple processing stages and stimulus intensities while simultaneously measuring perceptual performance. Such studies will be required to determine whether adaptive stochastic resonance operates as a transient compensatory mechanism at early nonlinear detection stages, how it interacts with downstream plasticity, and how beneficial variability can be distinguished from dysregulated cortical activity.

Clinical translation further raises the stakes. The initial therapeutic applications derived from the model suggest that adaptive noise modulation is not only theoretically significant, but also clinically relevant. However, large controlled trials and objective biomarkers are now required to define responder profiles and optimize treatment parameters.

Importantly, the stochastic resonance framework does not oppose existing models, but rather, it integrates induction, amplification, and conscious perception within a broader adaptive architecture. Whether stochastic resonance is the primary driver of tinnitus or represents a core mechanism within a multilevel process remains to be determined empirically. However, it is clear that the model has redefined tinnitus, shifting its perception from a purely maladaptive phenomenon to a potentially emergent property of adaptive sensory computation. This conceptual shift opens a new research program linking basic neuroscience, computational modeling, and clinical intervention within a coherent, testable framework.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. Generative AI tools were used during manuscript preparation to support drafting, language editing, text revision, summarization of published literature, and organizational aspects of the review. The author(s) critically reviewed, verified, and revised all generated content and take full responsibility for the accuracy, interpretation, and conclusions presented in the manuscript. No generative AI tools were used to generate, analyze, or interpret original data.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Hovannes Kulhandjian, California State University, Fresno, United States

Reviewed by: Jiayue Liu, Starkey Hearing Technologies, United States

Azadeh Borna, Iran University of Medical Sciences, Iran