Wind-robust sound event detection and denoising for bioacoustics

TL;DR

This paper introduces a wind-robust sound detection and denoising method for bioacoustics that improves detection accuracy and efficiency in ecological surveys by effectively handling transient wind noise without extra hardware.

Contribution

It proposes a novel transient noise estimation method using wavelet packet models combined with log-spectral subtraction, enhancing detection and denoising in wind-affected bioacoustic recordings.

Findings

Significantly reduced false alarms in bird surveys.

Improved call density estimation accuracy.

Enhanced denoising effectiveness under windy conditions.

Abstract

Sound recordings are used in various ecological studies, including acoustic wildlife monitoring. Such surveys require automatic detection of target sound events. However, current detectors, especially those relying on band-limited energy, are severely impacted by wind. The rapid dynamics of this noise invalidate standard noise estimators, and no satisfactory method for dealing with it exists in bioacoustics, where simple training and generalization between conditions are important. We propose to estimate the transient noise level by fitting short-term spectrum models to a wavelet packet representation. This estimator is then combined with log-spectral subtraction to stabilize the background level. The resulting adjusted wavelet series can be analysed by standard energy detectors. We use real monitoring data to tune this workflow, and test it on two acoustic surveys of birds. Additionally, we show how the estimator can be incorporated in a denoising method to restore sound. The proposed noise-robust workflow greatly reduced the number of false alarms in the surveys, compared to unadjusted energy detection. As a result, the survey efficiency (precision of the estimated call density) improved for both species. Denoising was also more effective when using the short-term estimate, whereas standard wavelet shrinkage with a constant noise estimate struggled to remove the effects of wind. In contrast to existing methods, the proposed estimator can adjust for transient broadband noises without requiring additional hardware or extensive tuning to each species. It improved the detection workflow based on very little training data, making it particularly attractive for detection of rare species.