Low-frequency sounds combined with motor imagery elicits a transient disruption of force performance: A path to neuromotor reprogramming?

The effectiveness of motor imagery (MI) training on sports performance is now well-documented. Recently, it has been proposed that a single session of MI combined with low frequency sound (LFS) might enhance muscle activation. However, the neural mechanisms underlying this effect remain unknown. We set up a test-retest intervention over the course of 2 consecutive days to evaluate the effect of (i) MI training (MI, n = 20), (ii) MI combined with LFS (MI + LFS, n = 20), and (iii) a control condition (CTRL, n = 20) on force torque produced across repeated maximal voluntary contractions of the quadriceps before (Pretest), after (Posttest) and at +12 h (Retention) post-intervention. We collected the integrated electromyograms of the quadriceps muscles, as well as brain electrical potentials during each experimental intervention. In the CTRL group, total force torque decreased from Pretest to Retention and from Posttest to Retention. By contrast, there was an increase between Posttest and Retention in both MI + LFS and MI groups (both ηP2 = 0.03, p < 0.05). Regression analyses further revealed a negative relationship between force performance and EEG activity in the MI + LFS group only. The data support a transient interference of LFS on cortical activity underlying the priming effects of MI practice on force performance. Findings are discussed in relation to the potential for motor reprogramming through MI combined with LFS.

A novel paradigm for measuring prediction abilities in a rat model using a speech-sound discrimination task.

The ability to predict and respond to upcoming stimuli is a critical skill for all animals, including humans. Prediction operates largely below conscious awareness to allow an individual to recall previously encountered stimuli and prepare an appropriate response, especially in language. The ability to predict upcoming words within typical speech patterns aids fluent comprehension, as conversational speech occurs quickly. Individuals with certain neurodevelopmental disorders such as autism and dyslexia have deficits in their ability to generate and use predictions. Rodent models are often used to investigate specific aspects of these disorders, but there is no existing behavioral paradigm that can assess prediction capabilities with complex stimuli like speech sounds. Thus, the present study modified an existing rapid speech sound discrimination paradigm to assess whether rats can form predictions of upcoming speech sound stimuli and utilize them to improve task performance. We replicated prior work showing that rats can discriminate between speech sounds presented at rapid rates. We also saw that rats responded exclusively to the target at slow speeds but began responding to the predictive cue in anticipation of the target as the speed increased, suggesting that they learned the predictive value of the cue and adjusted their behavior accordingly. This prediction task will be useful in assessing prediction deficits in rat models of various neurodevelopmental disorders through the manipulation of both genetic and environmental factors.

A preliminary study to identify critical factors for evaluating the effect of car-lock sounds on drivers.

Car-lock sounds are designed to inform the lock status of vehicles. However, drivers often experience a lack of confidence regarding whether the car is locked, and car thefts persistently occur, frequently attributed to unlocked doors. Without identification of critical factors for evaluating effects of car-lock sounds on drivers, a strategy to car-lock sound design with increased locking efficiency remains implicit. This study proposes a method to identify critical factors influencing drivers' perceived certainty of car-lock status and behaviours during car-locking. An experiment was conducted to simulate the locking process and verbal protocol analysis was employed to comprehend participants' cognitive processes and behaviours. The results show that mechanical sound yielded high certainty and few hesitations, while tonal and crisp sound elicited low certainty and frequent hesitations. Seven critical factors on participants' behaviours and cognitive processes were identified, which provides a data-driven approach for future research in car-lock sounds evaluation and design.

The effect of car-lock sounds on drivers is significant to inform the locking status of vehicles. However, the strategy for car-lock sounds evaluation remains implicit. This study proposes a method to identify critical factors on drivers' behaviours and cognitive processes that would inform further car-lock sounds evaluation and design.

Comparison of Speech Defects in Different Types of Malocclusion.

Introduction Speech has a great impact on human evolution, allowing for the widespread knowledge and advancement of tools. Difficulty in pronouncing one or more sounds is the most common speech impairment. Speech defects are more commonly associated with class III malocclusion patients (difficulty in pronouncing 's' and 't' sounds), the second in line is class II malocclusion (difficulty in pronouncing 's' and 'z' sounds), and speech distortions are least affected in class I malocclusion (difficulty in pronouncing 's' and 'Sh'). Most patients with dentofacial disharmonies and speech distortions need orthodontic care and orthognathic surgery to resolve their issues with mastication, aesthetics, and speech. Aims and objectives To compare and assess speech difficulties in different types of malocclusion. Materials and methods The study was conducted over 160 subjects for three and half months. All of them were evaluated for speech defects before they received orthodontic treatment. The main basis of this study is according to Angle's classification of malocclusion. The subjects were segregated according to Angle's classification of malocclusion. Malocclusion traits that are included in this study are Angle's class I, Angle's class II division I and division II, and Angle's class III. Results According to the results, out of 160 subjects, labio-dental speech defects are observed in 8% where n=13 of the study participants, linguodental speech defects are observed in 2% where n=3, lingua-alveolar speech defects are present in 54% where n=86, and bilabial speech defects are observed in 2% where n=3 of the study participants. Here 'n' represents the frequency of the subjects. Severe speech defects are seen in Angle's class III malocclusion. Results according to the type of malocclusion include: labio-dental speech defects are seen in 37.5% in class I, 25% in class II division I, 0% in class II division II, and 37.5% in class III. Linguodental speech defects are seen in class III malocclusion subjects only. Lingua-alveolar sounds are seen in 27.8% of class I, 29.6% of class II division I, 1.9% of class II division II, and 40.7% of class III. Bilabial speech defects are only seen in class II division I subjects. According to the results, only lingua-alveolar speech defects are statistically significant, and more severe speech defects were observed in class III malocclusion. Conclusion Speech plays an important role in affecting the quality of life of people. Different types of malocclusion traits are associated with different types of speech defects.

Transcranial Direct Current Stimulation (tDCS) over the left dorsolateral prefrontal cortex reduced attentional bias toward natural emotional sounds.

Previous research has indicated that the left dorsolateral prefrontal cortex (DLPFC) exerts an influence on attentional bias toward visual emotional information. However, it remains unclear whether the left DLPFC also play an important role in attentional bias toward natural emotional sounds. The current research employed the emotional spatial cueing paradigm, incorporating natural emotional sounds of considerable ecological validity as auditory cues. Additionally, high-definition transcranial direct current stimulation (HD-tDCS) was utilized to examine the impact of left dorsolateral prefrontal cortex (DLPFC) on attentional bias and its subcomponents, namely attentional engagement and attentional disengagement. The results showed that (1) compared to sham condition, anodal HD-tDCS over the left DLPFC reduced the attentional bias toward positive and negative sounds; (2) anodal HD-tDCS over the left DLPFC reduced the attentional engagement toward positive and negative sounds, whereas it did not affect attentional disengagement away from natural emotional sounds. Taken together, the present study has shown that left DLPFC, which was closely related with the top-down attention regulatory function, plays an important role in auditory emotional attentional bias.

Effects of looming audio on transcranial magnetic stimulation-induced phosphene perception.

Looming sounds are known to influence visual function in the brain, even as early as the primary visual cortex. However, despite evidence that looming sounds have a larger impact on cortical excitability than stationary sounds, the influence of varying looming strengths on visual ability remains unclear. Here, we aim to understand how these signals influence low-level visual function. Fourteen healthy undergraduate students participated. They were blindfolded and received transcranial magnetic stimulation (TMS) to the primary visual cortex following auditory stimulation with different strength looming sounds. Participants reported whether they perceived a phosphene, or an illusory visual percept, following TMS stimulation. We hypothesized that rates of phosphene activity would increase with increasing levels of looming strength. A linear mixed-effect model showed that phosphene activity was significantly higher at higher strength of looming (F(1, 69) = 5.33, p = .024) and at higher TMS pulse strength (F(1, 18) = 4.71, p = .043). However, there was also a significant interaction between looming strength and pulse strength (F(1, 69) = 4.33, p = .041). At lower levels of TMS strength, phosphene rate increased with looming strength, while at higher levels of TMS strength the effect was reversed. These results suggest a complex relationship between looming strength and cortical activity, potentially reflecting the mixed contribution of total auditory energy and the rate of changes. This work will enhance our ability to predict audiovisual interactions and may help improve auditory warning systems designed to capture visual attention.

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

Back to the future: the novel art of digital auscultation applied in a prospective observational study of critically ill Covid-19 patients.

BACKGROUND: The Covid-19 pandemic has caused immense pressure on Intensive Care Units (ICU). In patients with severe ARDS due to Covid-19, respiratory mechanics are important for determining the severity of lung damage. Lung auscultation could not be used during the pandemic despite its merit. The main objective of this study was to investigate associations between lung auscultatory sound features and lung mechanical properties, length of stay (LOS) and survival, in adults with severe Covid-19 ARDS. METHODS: Consecutive patients admitted to a large ICU between 2020 and 2021 (n = 173) were included. Digital stethoscopes obtained auscultatory sounds and stored them in an on-line database for replay and further processing using advanced AI techniques. Correlation and regression analysis explored relationships between digital auscultation findings and lung mechanics or the ICU outcome. The resulting annotated lung sounds database is also publicly available as supplementary material. RESULTS: The presence of squawks was associated with the ICU LOS, outcome and 90-day mortality. Other features (age, SOFA score & oxygenation index upon admission, minimum crackle entropy) had significant impact on outcome. Additional features affecting the 90-d survival were age and mean crackle entropy. Multivariate logistic regression showed that survival was affected by age, baseline SOFA, baseline oxygenation index and minimum crackle entropy. CONCLUSIONS: Respiratory mechanics were associated with various adventitious sounds, whereas the lung sound analytics and the presence of certain adventitious sounds correlated with the ICU outcome and the 90-d survival. Spectral features of crackles sounds can serve as prognostic factors for survival, highlighting the importance of digital auscultation.

Effects of Sound Interventions on the Mental Stress Response in Adults: Protocol for a Scoping Review.

BACKGROUND: Sound therapy methods have seen a surge in popularity, with a predominant focus on music among all types of sound stimulation. There is substantial evidence documenting the integrative impact of music therapy on psycho-emotional and physiological outcomes, rendering it beneficial for addressing stress-related conditions such as pain syndromes, depression, and anxiety. Despite these advancements, the therapeutic aspects of sound, as well as the mechanisms underlying its efficacy, remain incompletely understood. Existing research on music as a holistic cultural phenomenon often overlooks crucial aspects of sound therapy mechanisms, particularly those related to speech acoustics or the so-called "music of speech." OBJECTIVE: This study aims to provide an overview of empirical research on sound interventions to elucidate the mechanism underlying their positive effects. Specifically, we will focus on identifying therapeutic factors and mechanisms of change associated with sound interventions. Our analysis will compare the most prevalent types of sound interventions reported in clinical studies and experiments. Moreover, we will explore the therapeutic effects of sound beyond music, encompassing natural human speech and intermediate forms such as traditional poetry performances. METHODS: This review adheres to the methodological guidance of the Joanna Briggs Institute and follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist for reporting review studies, which is adapted from the Arksey and O'Malley framework. Our search strategy encompasses PubMed, Web of Science, Scopus, and PsycINFO or EBSCOhost, covering literature from 1990 to the present. Among the different study types, randomized controlled trials, clinical trials, laboratory experiments, and field experiments were included. RESULTS: Data collection began in October 2022. We found a total of 2027 items. Our initial search uncovered an asymmetry in the distribution of studies, with a larger number focused on music therapy compared with those exploring prosody in spoken interventions such as guided meditation or hypnosis. We extracted and selected papers using Rayyan software (Rayyan) and identified 41 eligible papers after title and abstract screening. The completion of the scoping review is anticipated by October 2024, with key steps comprising the analysis of findings by May 2024, drafting and revising the study by July 2024, and submitting the paper for publication in October 2024. CONCLUSIONS: In the next step, we will conduct a quality evaluation of the papers and then chart and group the therapeutic factors extracted from them. This process aims to unveil conceptual gaps in existing studies. Gray literature sources, such as Google Scholar, ClinicalTrials.gov, nonindexed conferences, and reference list searches of retrieved studies, will be added to our search strategy to increase the number of relevant papers that we cover. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54030.

Acoustic fish community in the Madeira Archipelago (North Atlantic Ocean): Characterization of sound diversity and daily patterns.

Marine ecosystems are increasingly subjected to anthropogenic pressures, which demands urgent monitoring plans. Understanding soundscapes can offer unique insights into the ocean status providing important information and revealing different sounds and their sources. Fishes can be prominent soundscape contributors, making passive acoustic monitoring (PAM) a potential tool to detect the presence of vocal fish species and to monitor changes in biodiversity. The major goal of this research was to provide a first reference of the marine soundscapes of the Madeira Archipelago focusing on fish sounds, as a basis for a long-term PAM program. Based on the literature, 102 potentially vocal and 35 vocal fish species were identified. Additionally 43 putative fish sound types were detected in audio recordings from two marine protected areas (MPAs) in the Archipelago: the Garajau MPA and the Desertas MPA. The Garajau MPA exhibited higher fish vocal activity, a greater variety of putative fish sound types and higher fish sound diversity. Lower abundance of sounds was found at night at both MPAs. Acoustic activity revealed a clear distinction between diurnal and nocturnal fish groups and demonstrated daily patterns of fish sound activity, suggesting temporal and spectral partitioning of the acoustic space. Pomacentridae species were proposed as candidates for some of the dominant sound types detected during the day, while scorpionfishes (Scorpaena spp.) were proposed as sources for some of the dominant nocturnal fish sounds. This study provides an important baseline about this community acoustic behaviour and is a valuable steppingstone for future non-invasive and cost-effective monitoring programs in Madeira.

Clinical features of audible upper airway secretions ("death rattle") in patients with cancer in the last days of life.

PURPOSE: Audible upper airway secretions ("death rattle") is a common problem in cancer patients at the end-of-life. However, there is little information about its clinical features. METHODS: This is a secondary analysis of a cluster randomised trial of clinically-assisted hydration in cancer patients in the last days of life. Patients were assessed 4 hourly for end-of-life problems (including audible secretions), which were recorded as present or absent, excepting restlessness/agitation, which was scored using the modified Richmond Agitation and Sedation Scale. Patients were followed up until death. RESULTS: 200 patients were recruited, and 186 patients died during the study period. Overall, 54.5% patients developed audible secretions at some point during the study, but only 34.5% patients had audible secretions at the time of death. The prevalence of audible secretions increased the closer to death, with a marked increase in the last 12-16 h of life (i.e. the prevalence of audible secretions was highest at the time of death). Of those with audible secretions at the time of death, 24 had had a previous episode that had resolved. Development of audible secretions was not associated with use of clinically-assisted hydration, but there was an association between audible secretions and restlessness/agitation, and audible secretions and pain. However, most patients with audible secretions were not restless/agitated, or in pain, when assessed. CONCLUSION: Audible secretions ("death rattle") are common in cancer patients at the end-of-life, but their natural history is extremely variable, with some patients experiencing multiple episodes during the terminal phase (although not necessarily experiencing an episode at the time of death).

Impact of Audio Data Compression on Feature Extraction for Vocal Biomarker Detection: Validation Study.

BACKGROUND: Vocal biomarkers, derived from acoustic analysis of vocal characteristics, offer noninvasive avenues for medical screening, diagnostics, and monitoring. Previous research demonstrated the feasibility of predicting type 2 diabetes mellitus through acoustic analysis of smartphone-recorded speech. Building upon this work, this study explores the impact of audio data compression on acoustic vocal biomarker development, which is critical for broader applicability in health care. OBJECTIVE: The objective of this research is to analyze how common audio compression algorithms (MP3, M4A, and WMA) applied by 3 different conversion tools at 2 bitrates affect features crucial for vocal biomarker detection. METHODS: The impact of audio data compression on acoustic vocal biomarker development was investigated using uncompressed voice samples converted into MP3, M4A, and WMA formats at 2 bitrates (320 and 128 kbps) with MediaHuman (MH) Audio Converter, WonderShare (WS) UniConverter, and Fast Forward Moving Picture Experts Group (FFmpeg). The data set comprised recordings from 505 participants, totaling 17,298 audio files, collected using a smartphone. Participants recorded a fixed English sentence up to 6 times daily for up to 14 days. Feature extraction, including pitch, jitter, intensity, and Mel-frequency cepstral coefficients (MFCCs), was conducted using Python and Parselmouth. The Wilcoxon signed rank test and the Bonferroni correction for multiple comparisons were used for statistical analysis. RESULTS: In this study, 36,970 audio files were initially recorded from 505 participants, with 17,298 recordings meeting the fixed sentence criteria after screening. Differences between the audio conversion software, MH, WS, and FFmpeg, were notable, impacting compression outcomes such as constant or variable bitrates. Analysis encompassed diverse data compression formats and a wide array of voice features and MFCCs. Wilcoxon signed rank tests yielded P values, with those below the Bonferroni-corrected significance level indicating significant alterations due to compression. The results indicated feature-specific impacts of compression across formats and bitrates. MH-converted files exhibited greater resilience compared to WS-converted files. Bitrate also influenced feature stability, with 38 cases affected uniquely by a single bitrate. Notably, voice features showed greater stability than MFCCs across conversion methods. CONCLUSIONS: Compression effects were found to be feature specific, with MH and FFmpeg showing greater resilience. Some features were consistently affected, emphasizing the importance of understanding feature resilience for diagnostic applications. Considering the implementation of vocal biomarkers in health care, finding features that remain consistent through compression for data storage or transmission purposes is valuable. Focused on specific features and formats, future research could broaden the scope to include diverse features, real-time compression algorithms, and various recording methods. This study enhances our understanding of audio compression's influence on voice features and MFCCs, providing insights for developing applications across fields. The research underscores the significance of feature stability in working with compressed audio data, laying a foundation for informed voice data use in evolving technological landscapes.

Investigation of Deepfake Voice Detection Using Speech Pause Patterns: Algorithm Development and Validation.

BACKGROUND: The digital era has witnessed an escalating dependence on digital platforms for news and information, coupled with the advent of "deepfake" technology. Deepfakes, leveraging deep learning models on extensive data sets of voice recordings and images, pose substantial threats to media authenticity, potentially leading to unethical misuse such as impersonation and the dissemination of false information. OBJECTIVE: To counteract this challenge, this study aims to introduce the concept of innate biological processes to discern between authentic human voices and cloned voices. We propose that the presence or absence of certain perceptual features, such as pauses in speech, can effectively distinguish between cloned and authentic audio. METHODS: A total of 49 adult participants representing diverse ethnic backgrounds and accents were recruited. Each participant contributed voice samples for the training of up to 3 distinct voice cloning text-to-speech models and 3 control paragraphs. Subsequently, the cloning models generated synthetic versions of the control paragraphs, resulting in a data set consisting of up to 9 cloned audio samples and 3 control samples per participant. We analyzed the speech pauses caused by biological actions such as respiration, swallowing, and cognitive processes. Five audio features corresponding to speech pause profiles were calculated. Differences between authentic and cloned audio for these features were assessed, and 5 classical machine learning algorithms were implemented using these features to create a prediction model. The generalization capability of the optimal model was evaluated through testing on unseen data, incorporating a model-naive generator, a model-naive paragraph, and model-naive participants. RESULTS: Cloned audio exhibited significantly increased time between pauses (P<.001), decreased variation in speech segment length (P=.003), increased overall proportion of time speaking (P=.04), and decreased rates of micro- and macropauses in speech (both P=.01). Five machine learning models were implemented using these features, with the AdaBoost model demonstrating the highest performance, achieving a 5-fold cross-validation balanced accuracy of 0.81 (SD 0.05). Other models included support vector machine (balanced accuracy 0.79, SD 0.03), random forest (balanced accuracy 0.78, SD 0.04), logistic regression, and decision tree (balanced accuracies 0.76, SD 0.10 and 0.72, SD 0.06). When evaluating the optimal AdaBoost model, it achieved an overall test accuracy of 0.79 when predicting unseen data. CONCLUSIONS: The incorporation of perceptual, biological features into machine learning models demonstrates promising results in distinguishing between authentic human voices and cloned audio.

Grouping sounds into evolving units for the purpose of historical language comparison.

Computer-assisted approaches to historical language comparison have made great progress during the past two decades. Scholars can now routinely use computational tools to annotate cognate sets, align words, and search for regularly recurring sound correspondences. However, computational approaches still suffer from a very rigid sequence model of the form part of the linguistic sign, in which words and morphemes are segmented into fixed sound units which cannot be modified. In order to bring the representation of sound sequences in computational historical linguistics closer to the research practice of scholars who apply the traditional comparative method, we introduce improved sound sequence representations in which individual sound segments can be grouped into evolving sound units in order to capture language-specific sound laws more efficiently. We illustrate the usefulness of this enhanced representation of sound sequences in concrete examples and complement it by providing a small software library that allows scholars to convert their data from forms segmented into sound units to forms segmented into evolving sound units and vice versa.

In linguistics, it is difficult to clearly draw the boundaries between the sounds in individual words. What one linguist may analyze as two sounds, another linguist might analyze at just one sound. Since the segmentation of words into sounds is crucial for many analyses in linguistics and since no perfect solution can be found, we offer a new representation that allows scholars to analyze the sounds in a word in a more flexible way that conforms to general standards while at the same time giving linguists enough flexibility to advance individual analyses.

Cortical responses to looming sources are explained away by the auditory periphery.

A wealth of behavioral evidence indicate that sounds with increasing intensity (i.e. appear to be looming towards the listener) are processed with increased attentional and physiological resources compared to receding sounds. However, the neurophysiological mechanism responsible for such cognitive amplification remains elusive. Here, we show that the large differences seen between cortical responses to looming and receding sounds are in fact almost entirely explained away by nonlinear encoding at the level of the auditory periphery. We collected electroencephalography (EEG) data during an oddball paradigm to elicit mismatch negativity (MMN) and others Event Related Potentials (EPRs), in response to deviant stimuli with both dynamic (looming and receding) and constant level (flat) differences to the standard in the same participants. We then combined a computational model of the auditory periphery with generative EEG methods (temporal response functions, TRFs) to model the single-participant ERPs responses to flat deviants, and used them to predict the effect of the same mechanism on looming and receding stimuli. The flat model explained 45% variance of the looming response, and 33% of the receding response. This provide striking evidence that difference wave responses to looming and receding sounds result from the same cortical mechanism that generate responses to constant-level deviants: all such differences are the sole consequence of their particular physical morphology getting amplified and integrated by peripheral auditory mechanisms. Thus, not all effects seen cortically proceed from top-down modulations by high-level decision variables, but can rather be performed early and efficiently by feed-forward peripheral mechanisms that evolved precisely to sparing subsequent networks with the necessity to implement such mechanisms.

Invertebrate sounds from photic to mesophotic coral reefs reveal vertical stratification and diel diversity.

Although mesophotic coral ecosystems account for approximately 80% of coral reefs, they remain largely unexplored due to their challenging accessibility. The acoustic richness within reefs has led scientists to consider passive acoustic monitoring as a reliable method for studying both altiphotic and mesophotic coral reefs. We investigated the relationship between benthic invertebrate sounds (1.5-22.5 kHz), depth, and benthic cover composition, key ecological factors that determine differences between altiphotic and mesophotic reefs. Diel patterns of snaps and peak frequencies were also explored at different depths to assess variations in biorhythms. Acoustic recorders were deployed at 20 m, 60 m, and 120 m depths across six islands in French Polynesia. The results indicated that depth is the primary driver of differences in broadband transient sound (BTS) soundscapes, with sound intensity decreasing as depth increases. At 20-60 m, sounds were louder at night. At 120 m depth, benthic activity rhythms exhibited low or highly variable levels of diel variation, likely a consequence of reduced solar irradiation. On three islands, a peculiar peak in the number of BTS was observed every day between 7 and 9 PM at 120 m, suggesting the presence of cyclic activities of a specific species. Our results support the existence of different invertebrate communities or distinct behaviors, particularly in deep mesophotic reefs. Overall, this study adds to the growing evidence supporting the use of passive acoustic monitoring to describe and understand ecological patterns in mesophotic reefs.

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds.

Introduction: Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics. Methods: In light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events. Results: Experimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS. Discussion: Our proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning.

OBJECTIVE: To develop a deep learning algorithm to perform multi-class classification of normal pediatric heart sounds, innocent murmurs, and pathologic murmurs. METHODS: We prospectively enrolled children under age 18 being evaluated by the Division of Pediatric Cardiology. Parents provided consent for a deidentified recording of their child's heart sounds with a digital stethoscope. Innocent murmurs were validated by a pediatric cardiologist and pathologic murmurs were validated by echocardiogram. To augment our collection of normal heart sounds, we utilized a public database of pediatric heart sound recordings (Oliveira, 2022). We propose two novel approaches for this audio classification task. We train a vision transformer on either Markov transition field or Gramian angular field image representations of the frequency spectrum. We benchmark our results against a ResNet-50 CNN trained on spectrogram images. RESULTS: Our final dataset consisted of 366 normal heart sounds, 175 innocent murmurs, and 216 pathologic murmurs. Innocent murmurs collected include Still's murmur, venous hum, and flow murmurs. Pathologic murmurs included ventricular septal defect, tetralogy of Fallot, aortic regurgitation, aortic stenosis, pulmonary stenosis, mitral regurgitation and stenosis, and tricuspid regurgitation. We find that the Vision Transformer consistently outperforms the ResNet-50 on all three image representations, and that the Gramian angular field is the superior image representation for pediatric heart sounds. We calculated a one-vs-rest multi-class ROC curve for each of the three classes. Our best model achieves an area under the curve (AUC) value of 0.92 ± 0.05, 0.83 ± 0.04, and 0.88 ± 0.04 for identifying normal heart sounds, innocent murmurs, and pathologic murmurs, respectively. CONCLUSION: We present two novel methods for pediatric heart sound classification, which outperforms the current standard of using a convolutional neural network trained on spectrogram images. To our knowledge, we are the first to demonstrate multi-class classification of pediatric murmurs. Multiclass output affords a more explainable and interpretable model, which can facilitate further model improvement in the downstream model development cycle and enhance clinician trust and therefore adoption.

Um, so, like, do speech disfluencies matter? A parametric evaluation of filler sounds and words.

This study evaluated how speech disfluencies affect perceived speaker effectiveness. Speeches with filler sounds and filler words at different rates of disfluencies (i.e., 0, 2, 5, and 12 per minute) were created and evaluated by a crowdsourcing service for survey-based research for the speaker's public speaking performance. Increased disfluencies, particularly filler sounds, significantly affected perceptions across most categories, notably at higher rates of filler sounds (i.e., 12 per minute). A low, but nonzero, rate of disfluencies (5 per minute) did not adversely affect perceived effectiveness. These findings suggest that although reducing filler sounds is crucial for optimizing perceived speaking effectiveness, a rate of five or fewer disfluencies per minute may be acceptable.

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.

Background and Objective: Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. Methods: PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Key Content and Findings: Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Conclusions: Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.