Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception.

Theories of auditory and visual scene analysis suggest the perception of scenes relies on the identification and segregation of objects within it, resembling a detail-oriented processing style. However, a more global process may occur while analyzing scenes, which has been evidenced in the visual domain. It is our understanding that a similar line of research has not been explored in the auditory domain; therefore, we evaluated the contributions of high-level global and low-level acoustic information to auditory scene perception. An additional aim was to increase the field's ecological validity by using and making available a new collection of high-quality auditory scenes. Participants rated scenes on 8 global properties (e.g., open vs. enclosed) and an acoustic analysis evaluated which low-level features predicted the ratings. We submitted the acoustic measures and average ratings of the global properties to separate exploratory factor analyses (EFAs). The EFA of the acoustic measures revealed a seven-factor structure explaining 57% of the variance in the data, while the EFA of the global property measures revealed a two-factor structure explaining 64% of the variance in the data. Regression analyses revealed each global property was predicted by at least one acoustic variable (R2 = 0.33-0.87). These findings were extended using deep neural network models where we examined correlations between human ratings of global properties and deep embeddings of two computational models: an object-based model and a scene-based model. The results support that participants' ratings are more strongly explained by a global analysis of the scene setting, though the relationship between scene perception and auditory perception is multifaceted, with differing correlation patterns evident between the two models. Taken together, our results provide evidence for the ability to perceive auditory scenes from a global perspective. Some of the acoustic measures predicted ratings of global scene perception, suggesting representations of auditory objects may be transformed through many stages of processing in the ventral auditory stream, similar to what has been proposed in the ventral visual stream. These findings and the open availability of our scene collection will make future studies on perception, attention, and memory for natural auditory scenes possible.

Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds.

Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological indication. While clinically important, crackles occur rarely in breathing signals relative to other phases and abnormalities of lung sounds, imposing a considerable class imbalance in developing learning methodologies for automated tracking and diagnosis of lung pathologies. The scarcity and clinical relevance of crackle sounds compel a need for exploring data augmentation techniques to enrich the space of crackle signals. Given their unique nature, the current study proposes a crackle-specific constrained synthetic sampling (CSS) augmentation that captures the geometric properties of crackles across different projected object spaces. We also outline a task-agnostic validation methodology that evaluates different augmentation techniques based on their goodness of fit relative to the space of original crackles. This evaluation considers both the separability of the manifold space generated by augmented data samples as well as a statistical distance space of the synthesized data relative to the original. Compared to a range of augmentation techniques, the proposed constrained-synthetic sampling of crackle sounds is shown to generate the most analogous samples relative to original crackle sounds, highlighting the importance of carefully considering the statistical constraints of the class under study.

Are acoustics enough? Semantic effects on auditory salience in natural scenes.

Auditory salience is a fundamental property of a sound that allows it to grab a listener's attention regardless of their attentional state or behavioral goals. While previous research has shed light on acoustic factors influencing auditory salience, the semantic dimensions of this phenomenon have remained relatively unexplored owing both to the complexity of measuring salience in audition as well as limited focus on complex natural scenes. In this study, we examine the relationship between acoustic, contextual, and semantic attributes and their impact on the auditory salience of natural audio scenes using a dichotic listening paradigm. The experiments present acoustic scenes in forward and backward directions; the latter allows to diminish semantic effects, providing a counterpoint to the effects observed in forward scenes. The behavioral data collected from a crowd-sourced platform reveal a striking convergence in temporal salience maps for certain sound events, while marked disparities emerge in others. Our main hypothesis posits that differences in the perceptual salience of events are predominantly driven by semantic and contextual cues, particularly evident in those cases displaying substantial disparities between forward and backward presentations. Conversely, events exhibiting a high degree of alignment can largely be attributed to low-level acoustic attributes. To evaluate this hypothesis, we employ analytical techniques that combine rich low-level mappings from acoustic profiles with high-level embeddings extracted from a deep neural network. This integrated approach captures both acoustic and semantic attributes of acoustic scenes along with their temporal trajectories. The results demonstrate that perceptual salience is a careful interplay between low-level and high-level attributes that shapes which moments stand out in a natural soundscape. Furthermore, our findings underscore the important role of longer-term context as a critical component of auditory salience, enabling us to discern and adapt to temporal regularities within an acoustic scene. The experimental and model-based validation of semantic factors of salience paves the way for a complete understanding of auditory salience. Ultimately, the empirical and computational analyses have implications for developing large-scale models for auditory salience and audio analytics.

Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures.

Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there has been tremendous improvement in the performance of sound event detection systems, although at the expense of costly data collection and labeling efforts. In fact, current state-of-the-art methods employ supervised training methods that leverage large amounts of data samples and corresponding labels in order to facilitate identification of sound category and time stamps of events. As an alternative, the current study proposes a semi-supervised method for generating pseudo-labels from unsupervised data using a student-teacher scheme that balances self-training and cross-training. Additionally, this paper explores post-processing which extracts sound intervals from network prediction, for further improvement in sound event detection performance. The proposed approach is evaluated on sound event detection task for the DCASE2020 challenge. The results of these methods on both "validation" and "public evaluation" sets of DESED database show significant improvement compared to the state-of-the art systems in semi-supervised learning.

Electrostatic Acoustic Sensor with an Impedance-Matched Diaphragm Characterized for Body Sound Monitoring.

Acoustic sensors are able to capture more incident energy if their acoustic impedance closely matches the acoustic impedance of the medium being probed, such as skin or wood. Controlling the acoustic impedance of polymers can be achieved by selecting materials with appropriate densities and stiffnesses as well as adding ceramic nanoparticles. This study follows a statistical methodology to examine the impact of polymer type and nanoparticle addition on the fabrication of acoustic sensors with desired acoustic impedances in the range of 1-2.2 MRayls. The proposed method using a design of experiments approach measures sensors with diaphragms of varying impedances when excited with acoustic vibrations traveling through wood, gelatin, and plastic. The sensor diaphragm is subsequently optimized for body sound monitoring, and the sensor's improved body sound coherence and airborne noise rejection are evaluated on an acoustic phantom in simulated noise environments and compared to electronic stethoscopes with onboard noise cancellation. The impedance-matched sensor demonstrates high sensitivity to body sounds, low sensitivity to airborne sound, a frequency response comparable to two state-of-the-art electronic stethoscopes, and the ability to capture lung and heart sounds from a real subject. Due to its small size, use of flexible materials, and rejection of airborne noise, the sensor provides an improved solution for wearable body sound monitoring, as well as sensing from other mediums with acoustic impedances in the range of 1-2.2 MRayls, such as water and wood.

Explicit-memory multiresolution adaptive framework for speech and music separation.

The human auditory system employs a number of principles to facilitate the selection of perceptually separated streams from a complex sound mixture. The brain leverages multi-scale redundant representations of the input and uses memory (or priors) to guide the selection of a target sound from the input mixture. Moreover, feedback mechanisms refine the memory constructs resulting in further improvement of selectivity of a particular sound object amidst dynamic backgrounds. The present study proposes a unified end-to-end computational framework that mimics these principles for sound source separation applied to both speech and music mixtures. While the problems of speech enhancement and music separation have often been tackled separately due to constraints and specificities of each signal domain, the current work posits that common principles for sound source separation are domain-agnostic. In the proposed scheme, parallel and hierarchical convolutional paths map input mixtures onto redundant but distributed higher-dimensional subspaces and utilize the concept of temporal coherence to gate the selection of embeddings belonging to a target stream abstracted in memory. These explicit memories are further refined through self-feedback from incoming observations in order to improve the system's selectivity when faced with unknown backgrounds. The model yields stable outcomes of source separation for both speech and music mixtures and demonstrates benefits of explicit memory as a powerful representation of priors that guide information selection from complex inputs.

Adaptation in the sensory cortex drives bistable switching during auditory stream segregation.

Current theories of perception emphasize the role of neural adaptation, inhibitory competition, and noise as key components that lead to switches in perception. Supporting evidence comes from neurophysiological findings of specific neural signatures in modality-specific and supramodal brain areas that appear to be critical to switches in perception. We used functional magnetic resonance imaging to study brain activity around the time of switches in perception while participants listened to a bistable auditory stream segregation stimulus, which can be heard as one integrated stream of tones or two segregated streams of tones. The auditory thalamus showed more activity around the time of a switch from segregated to integrated compared to time periods of stable perception of integrated; in contrast, the rostral anterior cingulate cortex and the inferior parietal lobule showed more activity around the time of a switch from integrated to segregated compared to time periods of stable perception of segregated streams, consistent with prior findings of asymmetries in brain activity depending on the switch direction. In sound-responsive areas in the auditory cortex, neural activity increased in strength preceding switches in perception and declined in strength over time following switches in perception. Such dynamics in the auditory cortex are consistent with the role of adaptation proposed by computational models of visual and auditory bistable switching, whereby the strength of neural activity decreases following a switch in perception, which eventually destabilizes the current percept enough to lead to a switch to an alternative percept.

Reference free auscultation quality metric and its trends.

Stethoscopes are used ubiquitously in clinical settings to 'listen' to lung sounds. The use of these systems in a variety of healthcare environments (hospitals, urgent care rooms, private offices, community sites, mobile clinics, etc.) presents a range of challenges in terms of ambient noise and distortions that mask lung signals from being heard clearly or processed accurately using auscultation devices. With advances in technology, computerized techniques have been developed to automate analysis or access a digital rendering of lung sounds. However, most approaches are developed and tested in controlled environments and do not reflect real-world conditions where auscultation signals are typically acquired. Without a priori access to a recording of the ambient noise (for signal-to-noise estimation) or a reference signal that reflects the true undistorted lung sound, it is difficult to evaluate the quality of the lung signal and its potential clinical interpretability. The current study proposes an objective reference-free Auscultation Quality Metric (AQM) which incorporates low-level signal attributes with high-level representational embeddings mapped to a nonlinear quality space to provide an independent evaluation of the auscultation quality. This metric is carefully designed to solely judge the signal based on its integrity relative to external distortions and masking effects and not confuse an adventitious breathing pattern as low-quality auscultation. The current study explores the robustness of the proposed AQM method across multiple clinical categorizations and different distortion types. It also evaluates the temporal sensitivity of this approach and its translational impact for deployment in digital auscultation devices.

Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors.

Many commercial and prototype devices are available for capturing body sounds that provide important information on the health of the lungs and heart; however, a standardized method to characterize and compare these devices is not agreed upon. Acoustic phantoms are commonly used because they generate repeatable sounds that couple to devices using a material layer that mimics the characteristics of skin. While multiple acoustic phantoms have been presented in literature, it is unclear how design elements, such as the driver type and coupling layer, impact the acoustical characteristics of the phantom and, therefore, the device being measured. Here, a design of experiments approach is used to compare the frequency responses of various phantom constructions. An acoustic phantom that uses a loudspeaker to generate sound and excite a gelatin layer supported by a grid is determined to have a flatter and more uniform frequency response than other possible designs with a sound exciter and plate support. When measured on an optimal acoustic phantom, three devices are shown to have more consistent measurements with added weight and differing positions compared to a non-optimal phantom. Overall, the statistical models developed here provide greater insight into acoustic phantom design for improved device characterization.

Implications of clinical variability on computer-aided lung auscultation classification.

Thanks to recent advances in digital stethoscopes and rapid adoption of deep learning techniques, there has been tremendous progress in the field of Computerized Auscultation Analysis (CAA). Despite these promising leaps, the deploy-ment of these technologies in real-world applications remains limited due to inherent challenges with properly interpreting clinical data, particularly auscultations. One of the limiting factors is the inherent ambiguity that comes with variability in clinical opinion, even from highly trained experts. The lack of unanimity in expert opinions is often ignored in developing machine learning techniques to automatically screen normal from abnormal lung signals, with most algorithms being developed and tested on highly curated datasets. To better understand the potential pitfalls this selective analysis could cause in deployment, the current work explores the impact of clinical opinion variability on algorithms to detect adventitious patterns in lung sounds when trained on gold-standard data. The study shows that uncertainty in clinical opinion introduces far more variability and performance drop than dissidence in expert judgments. The study also explores the feasibility of automatically flagging auscultation signals based on their estimated uncertainty, thereby recommending further reassessment as well as improving computer-aided analysis.

Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study.

BACKGROUND: Diagnosis of pneumonia remains challenging. Digitally recorded and remote human classified lung sounds may offer benefits beyond conventional auscultation, but it is unclear whether classifications differ between the two approaches. We evaluated concordance between digital and conventional auscultation. METHODS: We collected digitally recorded lung sounds, conventional auscultation classifications and clinical measures and samples from children with pneumonia (cases) in low-income and middle-income countries. Physicians remotely classified recordings as crackles, wheeze or uninterpretable. Conventional and digital auscultation concordance was evaluated among 383 pneumonia cases with concurrently (within 2 hours) collected conventional and digital auscultation classifications using prevalence-adjusted bias-adjusted kappa (PABAK). Using an expanded set of 737 cases that also incorporated the non-concurrently collected assessments, we evaluated whether associations between auscultation classifications and clinical or aetiological findings differed between conventional or digital auscultation using χ2 tests and logistic regression adjusted for age, sex and site. RESULTS: Conventional and digital auscultation concordance was moderate for classifying crackles and/or wheeze versus neither crackles nor wheeze (PABAK=0.50), and fair for crackles-only versus not crackles-only (PABAK=0.30) and any wheeze versus no wheeze (PABAK=0.27). Crackles were more common on conventional auscultation, whereas wheeze was more frequent on digital auscultation. Compared with neither crackles nor wheeze, crackles-only on both conventional and digital auscultation was associated with abnormal chest radiographs (adjusted OR (aOR)=1.53, 95% CI 0.99 to 2.36; aOR=2.09, 95% CI 1.19 to 3.68, respectively); any wheeze was inversely associated with C-reactive protein >40 mg/L using conventional auscultation (aOR=0.50, 95% CI 0.27 to 0.92) and with very severe pneumonia using digital auscultation (aOR=0.67, 95% CI 0.46 to 0.97). Crackles-only on digital auscultation was associated with mortality compared with any wheeze (aOR=2.70, 95% CI 1.12 to 6.25). CONCLUSIONS: Conventional auscultation and remotely-classified digital auscultation displayed moderate concordance for presence/absence of wheeze and crackles among cases. Conventional and digital auscultation may provide different classification patterns, but wheeze was associated with decreased clinical severity on both.

Effect of background clutter on neural discrimination in the bat auditory midbrain.

The discrimination of complex sounds is a fundamental function of the auditory system. This operation must be robust in the presence of noise and acoustic clutter. Echolocating bats are auditory specialists that discriminate sonar objects in acoustically complex environments. Bats produce brief signals, interrupted by periods of silence, rendering echo snapshots of sonar objects. Sonar object discrimination requires that bats process spatially and temporally overlapping echoes to make split-second decisions. The mechanisms that enable this discrimination are not well understood, particularly in complex environments. We explored the neural underpinnings of sonar object discrimination in the presence of acoustic scattering caused by physical clutter. We performed electrophysiological recordings in the inferior colliculus of awake big brown bats, to broadcasts of prerecorded echoes from physical objects. We acquired single unit responses to echoes and discovered a subpopulation of IC neurons that encode acoustic features that can be used to discriminate between sonar objects. We further investigated the effects of environmental clutter on this population's encoding of acoustic features. We discovered that the effect of background clutter on sonar object discrimination is highly variable and depends on object properties and target-clutter spatiotemporal separation. In many conditions, clutter impaired discrimination of sonar objects. However, in some instances clutter enhanced acoustic features of echo returns, enabling higher levels of discrimination. This finding suggests that environmental clutter may augment acoustic cues used for sonar target discrimination and provides further evidence in a growing body of literature that noise is not universally detrimental to sensory encoding.NEW & NOTEWORTHY Bats are powerful animal models for investigating the encoding of auditory objects under acoustically challenging conditions. Although past work has considered the effect of acoustic clutter on sonar target detection, less is known about target discrimination in clutter. Our work shows that the neural encoding of auditory objects was affected by clutter in a distance-dependent manner. These findings advance the knowledge on auditory object detection and discrimination and noise-dependent stimulus enhancement.

Resetting of Auditory and Visual Segregation Occurs After Transient Stimuli of the Same Modality.

In the presence of a continually changing sensory environment, maintaining stable but flexible awareness is paramount, and requires continual organization of information. Determining which stimulus features belong together, and which are separate is therefore one of the primary tasks of the sensory systems. Unknown is whether there is a global or sensory-specific mechanism that regulates the final perceptual outcome of this streaming process. To test the extent of modality independence in perceptual control, an auditory streaming experiment, and a visual moving-plaid experiment were performed. Both were designed to evoke alternating perception of an integrated or segregated percept. In both experiments, transient auditory and visual distractor stimuli were presented in separate blocks, such that the distractors did not overlap in frequency or space with the streaming or plaid stimuli, respectively, thus preventing peripheral interference. When a distractor was presented in the opposite modality as the bistable stimulus (visual distractors during auditory streaming or auditory distractors during visual streaming), the probability of percept switching was not significantly different than when no distractor was presented. Conversely, significant differences in switch probability were observed following within-modality distractors, but only when the pre-distractor percept was segregated. Due to the modality-specificity of the distractor-induced resetting, the results suggest that conscious perception is at least partially controlled by modality-specific processing. The fact that the distractors did not have peripheral overlap with the bistable stimuli indicates that the perceptual reset is due to interference at a locus in which stimuli of different frequencies and spatial locations are integrated.

Auditory salience using natural scenes: An online study.

Salience is the quality of a sensory signal that attracts involuntary attention in humans. While it primarily reflects conspicuous physical attributes of a scene, our understanding of processes underlying what makes a certain object or event salient remains limited. In the vision literature, experimental results, theoretical accounts, and large amounts of eye-tracking data using rich stimuli have shed light on some of the underpinnings of visual salience in the brain. In contrast, studies of auditory salience have lagged behind due to limitations in both experimental designs and stimulus datasets used to probe the question of salience in complex everyday soundscapes. In this work, we deploy an online platform to study salience using a dichotic listening paradigm with natural auditory stimuli. The study validates crowd-sourcing as a reliable platform to collect behavioral responses to auditory salience by comparing experimental outcomes to findings acquired in a controlled laboratory setting. A model-based analysis demonstrates the benefits of extending behavioral measures of salience to broader selection of auditory scenes and larger pools of subjects. Overall, this effort extends our current knowledge of auditory salience in everyday soundscapes and highlights the limitations of low-level acoustic attributes in capturing the richness of natural soundscapes.

Neural Encoding of Auditory Statistics.

The human brain extracts statistical regularities embedded in real-world scenes to sift through the complexity stemming from changing dynamics and entwined uncertainty along multiple perceptual dimensions (e.g., pitch, timbre, location). While there is evidence that sensory dynamics along different auditory dimensions are tracked independently by separate cortical networks, how these statistics are integrated to give rise to unified objects remains unknown, particularly in dynamic scenes that lack conspicuous coupling between features. Using tone sequences with stochastic regularities along spectral and spatial dimensions, this study examines behavioral and electrophysiological responses from human listeners (male and female) to changing statistics in auditory sequences and uses a computational model of predictive Bayesian inference to formulate multiple hypotheses for statistical integration across features. Neural responses reveal multiplexed brain responses reflecting both local statistics along individual features in frontocentral networks, together with global (object-level) processing in centroparietal networks. Independent tracking of local surprisal along each acoustic feature reveals linear modulation of neural responses, while global melody-level statistics follow a nonlinear integration of statistical beliefs across features to guide perception. Near identical results are obtained in separate experiments along spectral and spatial acoustic dimensions, suggesting a common mechanism for statistical inference in the brain. Potential variations in statistical integration strategies and memory deployment shed light on individual variability between listeners in terms of behavioral efficacy and fidelity of neural encoding of stochastic change in acoustic sequences.SIGNIFICANCE STATEMENT The world around us is complex and ever changing: in everyday listening, sound sources evolve along multiple dimensions, such as pitch, timbre, and spatial location, and they exhibit emergent statistical properties that change over time. In the face of this complexity, the brain builds an internal representation of the external world by collecting statistics from the sensory input along multiple dimensions. Using a Bayesian predictive inference model, this work considers alternative hypotheses for how statistics are combined across sensory dimensions. Behavioral and neural responses from human listeners show the brain multiplexes two representations, where local statistics along each feature linearly affect neural responses, and global statistics nonlinearly combine statistical beliefs across dimensions to shape perception of stochastic auditory sequences.

Natural Statistics as Inference Principles of Auditory Tuning in Biological and Artificial Midbrain Networks.

Bats provide a powerful mammalian model to explore the neural representation of complex sounds, as they rely on hearing to survive in their environment. The inferior colliculus (IC) is a central hub of the auditory system that receives converging projections from the ascending pathway and descending inputs from auditory cortex. In this work, we build an artificial neural network to replicate auditory characteristics in IC neurons of the big brown bat. We first test the hypothesis that spectro-temporal tuning of IC neurons is optimized to represent the natural statistics of conspecific vocalizations. We estimate spectro-temporal receptive fields (STRFs) of IC neurons and compare tuning characteristics to statistics of bat calls. The results indicate that the FM tuning of IC neurons is matched with the statistics. Then, we investigate this hypothesis on the network optimized to represent natural sound statistics and to compare its output with biological responses. We also estimate biomimetic STRFs from the artificial network and correlate their characteristics to those of biological neurons. Tuning properties of both biological and artificial neurons reveal strong agreement along both spectral and temporal dimensions, and suggest the presence of nonlinearity, sparsity, and complexity constraints that underlie the neural representation in the auditory midbrain. Additionally, the artificial neurons replicate IC neural activities in discrimination of social calls, and provide simulated results for a noise robust discrimination. In this way, the biomimetic network allows us to infer the neural mechanisms by which the bat's IC processes natural sounds used to construct the auditory scene.

Computational framework for investigating predictive processing in auditory perception.

BACKGROUND: The brain tracks sound sources as they evolve in time, collecting contextual information to predict future sensory inputs. Previous work in predictive coding typically focuses on the perception of predictable stimuli, leaving the implementation of these same neural processes in more complex, real-world environments containing randomness and uncertainty up for debate. NEW METHOD: To facilitate investigation into the perception of less tightly-controlled listening scenarios, we present a computational model as a tool to ask targeted questions about the underlying predictive processes that connect complex sensory inputs to listener behavior and neural responses. In the modeling framework, observed sound features (e.g. pitch) are tracked sequentially using Bayesian inference. Sufficient statistics are inferred from past observations at multiple time scales and used to make predictions about future observation while tracking the statistical structure of the sensory input. RESULTS: Facets of the model are discussed in terms of their application to perceptual research, and examples taken from real-world audio demonstrate the model's flexibility to capture a variety of statistical structures along various perceptual dimensions. COMPARISON WITH EXISTING METHODS: Previous models are often targeted toward interpreting a particular experimental paradigm (e.g., oddball paradigm), perceptual dimension (e.g., pitch processing), or task (e.g., speech segregation), thus limiting their ability to generalize to other domains. The presented model is designed as a flexible and practical tool for broad application. CONCLUSION: The model is presented as a general framework for generating new hypotheses and guiding investigation into the neural processes underlying predictive coding of complex scenes.

Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings.

Chest auscultation is a widely used clinical tool for respiratory disease detection. The stethoscope has undergone a number of transformative enhancements since its invention, including the introduction of electronic systems in the last two decades. Nevertheless, stethoscopes remain riddled with a number of issues that limit their signal quality and diagnostic capability, rendering both traditional and electronic stethoscopes unusable in noisy or non-traditional environments (e.g., emergency rooms, rural clinics, ambulatory vehicles). This work outlines the design and validation of an advanced electronic stethoscope that dramatically reduces external noise contamination through hardware redesign and real-time, dynamic signal processing. The proposed system takes advantage of an acoustic sensor array, an external facing microphone, and on-board processing to perform adaptive noise suppression. The proposed system is objectively compared to six commercially-available acoustic and electronic devices in varying levels of simulated noisy clinical settings and quantified using two metrics that reflect perceptual audibility and statistical similarity, normalized covariance measure (NCM) and magnitude squared coherence (MSC). The analyses highlight the major limitations of current stethoscopes and the significant improvements the proposed system makes in challenging settings by minimizing both distortion of lung sounds and contamination by ambient noise.

Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope.

Electronic stethoscopes offer several advantages over conventional acoustic stethoscopes, including noise reduction, increased amplification, and ability to store and transmit sounds. However, the acoustical characteristics of electronic and acoustic stethoscopes can differ significantly, introducing a barrier for clinicians to transition to electronic stethoscopes. This work proposes a method to process lung sounds recorded by an electronic stethoscope, such that the sounds are perceived to have been captured by an acoustic stethoscope. The proposed method calculates an electronic-to-acoustic stethoscope filter by measuring the difference between the average frequency responses of an acoustic and an electronic stethoscope to multiple lung sounds. To validate the method, a change detection experiment was conducted with 51 medical professionals to compare filtered electronic, unfiltered electronic, and acoustic stethoscope lung sounds. Participants were asked to detect when transitions occurred in sounds comprising several sections of the three types of recordings. Transitions between the filtered electronic and acoustic stethoscope sections were detected, on average, by chance (sensitivity index equal to zero) and also detected significantly less than transitions between the unfiltered electronic and acoustic stethoscope sections ( ), demonstrating the effectiveness of the method to filter electronic stethoscopes to mimic an acoustic stethoscope. This processing could incentivize clinicians to adopt electronic stethoscopes by providing a means to shift between the sound characteristics of acoustic and electronic stethoscopes in a single device, allowing for a faster transition to new technology and greater appreciation for the electronic sound quality.

An objective measure of signal quality for pediatric lung auscultations.

A stethoscope is a ubiquitous tool used to 'listen' to sounds from the chest in order to assess lung and heart conditions. With advances in health technologies including digital devices and new wearable sensors, access to these sounds is becoming easier and abundant; yet proper measures of signal quality do not exist. In this work, we develop an objective quality metric of lung sounds based on low-level and high-level features in order to independently assess the integrity of the signal in presence of interference from ambient sounds and other distortions. The proposed metric outlines a mapping of auscultation signals onto rich low-level features extracted directly from the signal which capture spectral and temporal characteristics of the signal. Complementing these signal-derived attributes, we propose high-level learnt embedding features extracted from a generative auto-encoder trained to map auscultation signals onto a representative space that best captures the inherent statistics of lung sounds. Integrating both low-level (signal-derived) and high-level (embedding) features yields a robust correlation of 0.85 to infer the signal-to-noise ratio of recordings with varying quality levels. The method is validated on a large dataset of lung auscultation recorded in various clinical settings with controlled varying degrees of noise interference. The proposed metric is also validated against opinions of expert physicians in a blind listening test to further corroborate the efficacy of this method for quality assessment.