Snoring has long been treated as little more than a nuisance, the soundtrack of a restless night and a frequent source of complaints from bed partners. A new scoping review published in Machine Learning with Applications suggests it may be far more than that: a rich acoustic biomarker capable of revealing obstructive sleep apnea, a disorder that affects more than one billion adults worldwide and remains dangerously underdiagnosed. The review, led by Blessing C. Uzo of the University of Nigeria and colleagues, systematically examined 29 studies published between January 2015 and August 2025 that applied machine learning or deep learning to snoring and sleep-related audio, and its verdict is both exciting and sobering.
Obstructive sleep apnea occurs when the upper airway repeatedly collapses during sleep, cutting off breathing and fragmenting rest in ways that raise the risk of hypertension, cardiovascular disease, and cognitive decline. The diagnostic gold standard, polysomnography, requires an overnight stay in a sleep laboratory, specialized equipment, and trained technicians, making it far too resource-intensive for large-scale screening, particularly in low-resource settings. Snoring, produced by turbulent airflow through a narrowed airway, carries the physiological fingerprints of that very obstruction. Because it can be captured with nothing more exotic than a smartphone or a portable sensor placed at the bedside, researchers have increasingly turned to artificial intelligence to decode it.
The technical results compiled in the review are striking. Deep learning models, particularly convolutional neural networks trained on Mel spectrograms of snoring audio, have repeatedly reported accuracies above 90 percent on internal test data. A VGG19-based CNN applied to the public PSG-Audio dataset achieved 95.4 percent accuracy, while an Audio Spectrogram Transformer applied to simulated snoring sounds reached 92.6 percent accuracy with an area under the curve of 0.93. The field has clearly evolved from shallow classifiers such as support vector machines and random forests, which appeared in ten of the reviewed studies, toward deep and hybrid architectures that together accounted for 19 of the 29 studies, or 65.5 percent of the literature.
Behind those headline numbers lies a consistent pattern of acoustic feature engineering. Mel-frequency cepstral coefficients, derived by projecting the short-time Fourier transform of the snoring signal onto a filterbank that mimics the frequency resolution of the human cochlea, were the most widely used input, appearing in 18 of 29 studies. Mel and log-Mel spectrograms, which preserve the full two-dimensional time-frequency structure of the sound, appeared in 11 studies and dominate modern CNN pipelines. Older, physiologically motivated descriptors such as linear predictive coding, which models the vocal tract as an all-pole filter and captures the resonant properties of the upper airway, and simple measures like zero crossing rate, short-time energy, and the PR800 power ratio that separates apneic snores from benign ones, rounded out the feature landscape.
Yet the review identifies three interlocking problems that stand between these impressive laboratory numbers and genuine clinical utility. The first is explainability. Fully 21 of the 29 studies, or 72.4 percent, reported no explanation method at all, leaving their high-performing models as inscrutable black boxes. Only six studies used feature importance rankings, one employed an attention mechanism to highlight salient spectrogram regions, and just one, a Taiwanese study combining snoring events with anthropometric data, applied SHAP, a rigorous post-hoc attribution technique. Not a single study applied Grad-CAM or LIME, the standard tools for interpreting deep networks, to spectrogram-based models.
That absence matters because clinicians cannot act on predictions they cannot interrogate. The review’s authors argue that meaningful explainability in this domain must answer specific questions: which temporal segments of a recording predict an apnea event, which frequency bands correspond to anatomical narrowing, and whether a model’s reasoning maps onto known mechanisms of airway collapse. Handcrafted features such as LPC or zero crossing rate carry direct physiological meaning, making them easier to interpret, while spectrogram heatmaps generated by post-hoc tools show where a model looked without revealing what the highlighted pattern means physiologically, whether soft-palate vibration, turbulent airflow, or simply recording noise.
The second problem is data. Twenty-one of the 29 studies, 72.4 percent, relied entirely on private, non-public datasets, and only four used exclusively public data. Geographic concentration compounds the issue: 75.9 percent of the studies came from Asia, with 12 from China and five from South Korea, while no studies at all originated from Africa, South America, or the Middle East. Sample sizes were frequently small, with seven studies enrolling fewer than 50 participants, including one with just 12 subjects and another with five. Only two studies exceeded 1,000 participants.
The third and perhaps most damning problem is validation. Every single one of the 29 studies relied exclusively on internal validation, using data from the same source for training and testing, and none performed external validation on an independent cohort from a different institution or population. The consequences of this choice are vividly illustrated by one Chinese study that reported 99.31 percent accuracy on 50 participants using a simple hold-out split, then watched that figure collapse to 66.29 percent when the same dataset was evaluated with leave-one-subject-out cross-validation, the more rigorous standard that prevents segments from the same sleeper from appearing in both training and test folds. Meanwhile, the largest studies reported far more modest results, with accuracies of 74 to 86 percent, suggesting that many of the field’s headline figures are upper-bound estimates inflated by small cohorts and leaky validation designs.
The review also flags inconsistent metric reporting as a barrier to progress. Sensitivity, specificity, and F1-score, the measures that matter most for imbalanced clinical datasets where normal breathing events vastly outnumber apneic ones, were absent from 62.1 percent of the studies. Without those metrics, it is impossible to know whether a model that claims high accuracy would actually catch the patients who need help or simply excel at labeling healthy sleepers as healthy.
The path forward, the authors argue, requires concrete changes rather than vague exhortations. The field needs a multi-center, openly licensed snoring corpus with standardized recording protocols specifying microphone type, placement, sampling rate, and noise floor, labeled at the level of individual respiratory events rather than whole-night summaries. Explainability should be built into model design from the start, with promising directions including prototype-based explanations that present clinicians with representative reference snores for each severity class. Subject-wise validation should become the minimum accepted standard, and true external validation across institutions, countries, and ethnic groups should be a prerequisite for any claim of clinical readiness. If those standards take hold, the humble snore, recorded on an ordinary smartphone, could one day become the front line of a global screening effort against one of medicine’s most overlooked epidemics. Until then, the review concludes, the dazzling accuracy figures scattered through the literature should be read as proof of concept, not as evidence that the technology is ready for the clinic.
Subject of Research: Machine learning detection of obstructive sleep apnea from snoring audio
Article Title: Snoring-based audio analysis for obstructive sleep apnea detection: A scoping review of machine learning models and explainability approaches
Article References: Uzo, B. C., Udanor, C. N., Bande, P. S., Obayi, A. A., Abhadiomhen, S. E., Ugwuoke, N., Obaido, G., & Ogbuokiri, B. (2026). Snoring-based audio analysis for obstructive sleep apnea detection: A scoping review of machine learning models and explainability approaches. Machine Learning with Applications, 26, Article 101029. https://doi.org/10.1016/j.mlwa.2026.101029
Image Credits: AI Generated
DOI: 10.1016/j.mlwa.2026.101029
Keywords: obstructive sleep apnea, snoring, machine learning, deep learning, explainable AI, acoustic biomarkers, Mel spectrograms, MFCC, polysomnography, scoping review, external validation, smartphone screening
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Blake Davidson. (October 2, 2026). Your Snore Could Reveal Sleep Apnea, But AI Isn’t Ready Yet. Scienmag. https://scienmag.com/your-snore-could-reveal-sleep-apnea-but-ai-isnt-ready-yet/
Blake Davidson. “Your Snore Could Reveal Sleep Apnea, But AI Isn’t Ready Yet.” Scienmag, 2 October 2026, https://scienmag.com/your-snore-could-reveal-sleep-apnea-but-ai-isnt-ready-yet/. Accessed 2 October 2026.
Blake Davidson. “Your Snore Could Reveal Sleep Apnea, But AI Isn’t Ready Yet.” Scienmag. October 2, 2026. https://scienmag.com/your-snore-could-reveal-sleep-apnea-but-ai-isnt-ready-yet/
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Tags: acoustic biomarkersacoustic signal processing for healthAI-based screening methodsaudio analysis for sleep healthchallenges in sleep apnea diagnosisdeep learningdeep learning for obstructive sleep apneaexplainable AIexternal validationlow-resource sleep screening solutionsMachine learningmachine learning in sleep disorder diagnosisMel spectrogramsMFCCnon-invasive sleep disorder detectionobstructive sleep apneapolysomnographyscoping reviewsleep apnea detectionsleep study alternativessmartphone screeningsnoringsnoring acoustic biomarkersunderdiagnosis of sleep apnea
