Bio-Acoustic Distress Detection

• Bio-acoustic distress detection is the process of analyzing acoustic emissions to identify distress in organisms such as plants, humans, and animals.
• It involves extracting spectral, temporal, and nonlinear features using techniques like MFCCs, PCA, and deep learning to quantify distress signals.
• Advanced machine learning frameworks and explainability tools enable robust, cross-species analysis and real-time clinical and ecological applications.

Bio-acoustic distress detection refers to the automated or analytical identification, quantification, and interpretation of distress states in biological organisms based on acoustic emissions. These emissions arise from physiological or psychological disturbance (e.g., mechanical stress in plants, respiratory or neurological pathology in humans and animals) and encode information in complex spectral, temporal, and nonlinear acoustic features. The domain encompasses methodologies ranging from high-frequency ultrasonic recordings in plants to prosodic and spectral voice analysis in humans, and extends to cross-species and ecological perspectives.

1. Physiological and Environmental Origins of Bio-Acoustic Distress Signals

Bio-acoustic distress signals originate when an organism experiences acute threat or dysfunction, triggering characteristic vocalizations, body sounds, or ultra-/infrasonic emissions. In plants, severe dehydration causes xylem embolism (cavitation events) and subsequent cellular damage, producing ultrasonic acoustic emissions (AEs) that manifest as discrete high-frequency spikes, followed by lower-frequency, more variable emissions linked to terminal membrane rupture and cell lysis (Lamacque et al., 2021). In mammalian neonates, respiratory or neurological distress alters the structure of cries or chest sounds: increased expiratory effort, voice instability, frequency shifts (hyperphonation), and the emergence of turbulence (dysphonation) are typical (Onu et al., 2023, Grooby et al., 2022). Human and non-human animal distress vocalizations encode arousal intensity through pitch (meanF₀, rangeF₀), deterministic chaos (broadband, unstable energy), harmonicity (HNR), and upper-band spectral prominences (SP₂/SP₃) (Thévenet et al., 2023).

Environmental factors, sensor placement, and developmental context significantly modulate the signal-to-noise ratio and the interpretability of distress markers, as evidenced by multicenter neonatal cry databases and real-world telemedicine deployment studies (Onu et al., 2023, Rashid et al., 2020).

2. Acoustic Feature Extraction and Quantification

Feature extraction in bio-acoustic distress detection targets multidimensional signal properties:

• Spectral Features: Mel-frequency cepstral coefficients (MFCCs), spectral centroid, spectral flatness, log-Mel-band energies, and harmonic-to-noise ratio (HNR) provide discrete bases for representation of vocal or emission spectra. HNR, for instance, is computed as

$$\mathrm{HNR_{dB}} = 10\,\log_{10} \frac{\sum_t |s_{\rm harmonic}(t)|^2}{\sum_t |s(t) - s_{\rm harmonic}(t)|^2}$$

capturing the ratio of periodic to aperiodic energy (Thévenet et al., 2023).

• Temporal and Nonlinear Features: Event duration, chaos percentage (frames with broadband noise), jitter/shimmer (cycle-to-cycle variability in period/amplitude), pause duration, and articulation rate are crucial. For chaos,

$$\mathrm{Chaos\%} = 100 \times \frac{\text{frames classified as chaos}}{\text{total frames}}$$

is used as a unified metric for broadband nonlinear phenomena (Thévenet et al., 2023).

• Prosodic and Vital Sign Features: Fundamental frequency statistics ($\overline{F_0}$, $F_{0,\mathrm{std}}$, range), intensity envelope, voice/pause ratios, respiratory rate (chest sounds), and autocorrelation-derived heart rate are routinely computed (Länzlinger et al., 17 Nov 2025, Grooby et al., 2022, Rashid et al., 2020).
• Cry-Specific Biomarkers: For neonatal distress, hyperphonation (framewise $F_0 > 1\,000$ Hz), dysphonation (increased spectral flatness), glide, vibrato, and melody-type descriptors are aggregated over cry units. The detection of such patterns (e.g., proportion of cry time with elevated $F_0$ or turbulence) correlates with clinical indicators of injury or distress (Onu et al., 2023).

3. Statistical and Machine Learning Frameworks

Data-driven bio-acoustic distress detection leverages dimensionality reduction, classification, and explainability strategies:

• Principal and Partial Least Squares Analysis: In plant AE studies, principal component analysis (PCA) over 15 event features is used for phase separation and classification of AE origin (hydraulic vs. non-hydraulic), with identification rules (e.g., LV3 > 0 & LV4 < 0) validated across independent recording systems (Lamacque et al., 2021). In animal/human studies, PCA and PLS logistic regression identify acoustic principal axes (e.g., pitch, chaos/SP prominence, HNR/jitter) and reveal which features most reliably map to behavioral responses or distress ratings (Thévenet et al., 2023).
• Ensemble Boosting and SVMs: Boosting techniques, specifically RUSBoost (random undersampling plus AdaBoost over shallow trees), address class imbalance in neonatal distress detection, optimizing accuracy, sensitivity, and specificity (up to 85.0%, 66.7%, and 81.8%, respectively) (Grooby et al., 2022). Support vector machines with linear kernels, using high-dimensional acoustic/prosodic inputs, yield high-accuracy distress classification in telephonic speech (86.4% accuracy, AUC 92.0%) (Rashid et al., 2020).
• Deep Transfer Learning Pipelines: The Roseline system implements a three-stage transfer learning scheme—self-supervised pretraining, domain adaptation, then supervised fine-tuning—on a VGG-derived large audio model for infant cry-based injury detection, achieving AUC 92.5% with interpretable biomarker extraction (Onu et al., 2023).
• Structured Linkage Frameworks: IHearYou employs a hierarchical, explainable mapping from low-level audio metrics (LLDs) through high-level features (HLDs), biomarker abstractions, and DSM-5 indicator scores, with FDR correction and stratified statistical associations for clinical depression detection (Länzlinger et al., 17 Nov 2025).

4. Comparative and Cross-species Perspectives

Bio-acoustic distress detection research reveals both universals and divergences in distress encoding and decoding across taxa:

• Nonlinear Acoustic Markers as Universals: Deterministic chaos, low harmonicity, and high upper-band spectral energy drive crocodile orienting responses to infant hominid cries, independent of species and more reliably than pitch (Thévenet et al., 2023). This suggests that automated detectors emphasizing chaos%, HNR, and spectral prominences (SP₂/SP₃) will best generalize across taxa.
• Species-specific Heuristics: Humans assign distress based primarily on pitch metrics (mean/max $F_0$, range) and its variability, which distinguishes within-species emotional intensity but may yield misclassification when applied cross-species, as demonstrated in bonobo and chimpanzee infant cry playback studies (Thévenet et al., 2023).
• Cry as a Universal Vital Sign: In clinical neonatology, both plant and animal/infant systems leverage real-time, sensor-based distress detection frameworks to provide dynamic, quantitative, and non-invasive indices of health, functional breakdown, or mortality risk (Lamacque et al., 2021, Onu et al., 2023).

5. Practical Implementation and Deployment Considerations

Robust bio-acoustic distress detection requires precise attention to instrumentation, sampling protocols, preprocessing, and real-time operability:

Component	Plant AE System (Lamacque et al., 2021)	Human/Animal Systems (Grooby et al., 2022, Onu et al., 2023)
Sensor	Ultrasonic piezo sensor (150–800 kHz, ≥2 MHz sampling)	Electret microphone, digital stethoscope, smartphone
Placement	Debarked stem patch, clamped, grease coupling	Chest wall, ~10–15 cm from mouth (for infants), fixed
Preprocessing	AE thresholding, real-time feature extraction, PCA	LPF+resample, T–F transforms, blind separation
Features	15 AE time-frequency-spatial descriptors	MFCCs, band powers, time/statistics, cry biomarkers
Classification	PCA quadrant rule (AE1/2), logistic curve for risk	Boosted trees, SVM, self-supervised CNN
Output	Dynamic vulnerability curve, system alarms	Risk probability, sensitivity/specificity, clinical UI

Best practices involve (a) sensor calibration, (b) vibration/noise isolation, (c) time-synchronized logging, and (d) periodic recalibration to account for biological or hardware drift (Lamacque et al., 2021). In low-resource settings, deployment on smartphones or edge compute modules enables cost-effective triage, as in point-of-care neonatology (Onu et al., 2023).

6. Interpretation, Explainability, and Clinical or Ecological Impact

Explainability frameworks such as the Linkage Framework (IHearYou) and Roseline feature importance maps connect detected acoustic anomalies to clinically documented distress states or behavioral outcomes (Länzlinger et al., 17 Nov 2025, Onu et al., 2023). For plant systems, the separation of xylem hydraulic failure from terminal cellular damage (AE1 vs AE2) provides mechanism-resolved drought vulnerability curves, translating sound recordings into actionable agronomic indices (Lamacque et al., 2021).

A plausible implication is that, across domains, the effectiveness of detection is enhanced by privileging nonlinear and aggregate spectral features over narrowly species-specific cues. The transition from black-box pathology detection toward rule-based, indicator-grade proxy measurement enhances trust, auditability, and integrability into clinical or ecological workflows.

7. Limitations and Ongoing Challenges

Current systems report high accuracy under controlled or semi-controlled conditions but face challenges: (1) reduced performance for mild distress or high ambient noise, (2) cross-site and cross-population variability, (3) annotation and ground-truthing burdens, and (4) ecological or clinical heterogeneity (Onu et al., 2023, Rashid et al., 2020). Interspecies decoding asymmetries (e.g., humans vs. crocodiles) further call for tailored feature selection and validation. Future directions include refinement of biomarker sets, expanded population testing, integration with multi-modal sensing, and advances in privacy-preserving, on-device analytical pipelines (Onu et al., 2023, Länzlinger et al., 17 Nov 2025, Lamacque et al., 2021).