Noise-Resilient Bioacoustics Feature Extraction Methods and Their Implications on Audio Classification Performance: Systematic Review
Bibliographic record
Abstract
Background: Bioacoustics classification plays a crucial role in ecological surveillance and neonatal health monitoring. Infant cry analysis can aid early health diagnostics, while ecological acoustics informs conservation. However, the presence of environmental noise, signal variability, and limited annotated datasets often hinders model reliability and deployment. Robust feature extraction and denoising techniques have become critical for improving model robustness, enabling more accurate interpretation of acoustic events across diverse bioacoustic domains under real-world conditions. Objective: This review systematically evaluates advancements in noise-resilient feature extraction and denoising techniques for bioacoustics classification. Specifically, it explores methodological trends, model types, cross-domain transferability between clinical and ecological applications, and evidence for real-world deployment. Methods: A systematic review was conducted by searching 8 electronic databases, including IEEE Xplore, ScienceDirect, Web of Science, ACM Digital Library, and Scopus, through December 2024. Eligible studies entailed audio-based classification models and applied empirical or computational evaluations of bioacoustics classification using machine learning or deep learning methods. In addition, studies also included explicit or implicit consideration of noise. Two reviewers independently screened studies, extracted data, and assessed quality. Risk of bias was assessed using a customized tool, and reporting quality was evaluated using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist. Results: Of the 5462 records, 132 studies met the eligibility criteria. The majority (112/132, 84.8%) of studies focused on model innovation, with deep learning and hybrid approaches being the most dominant. Feature extraction played a critical role, with 96.2% (127/132) of studies clearly demonstrating feature extraction. Mel frequency cepstral coefficients, spectrograms, and filter bank-based representations were the most common feature representations. Nearly half (62/132, 47%) of the studies incorporated noise-resilient methods, such as adaptive deep models, wavelet transforms, and spectral filtering. However, only 14.4% (19/132) demonstrated real-world deployment across neonatal care and ecological field settings. Conclusions: The integration of noise-resilient techniques has significantly improved classification performance, but real-world deployment and proper use of denoising strategies in various datasets remain limited. Cross-domain synthesis reveals shared challenges, including dataset heterogeneity, inconsistent reporting, and reliance on synthetic noise. Future work should prioritize harmonized benchmarks, cross-domain generalization, and deployment, as well as opportunities for transferability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".