2-Dimensional spectral gating for denoising bioacoustics recordings
Organizations: Mila - Quebec AI Institute · McGill University · Pioneer Centre for AI, University of Copenhagen · Stockholm University
Abstract
Isolating vocalizations from noise in bioacoustics recordings is a prerequisite to many ecological analyses, including species identification, animal communication understanding, and individual or population-level variability studies. However, when recordings are acquired in open environments, vocalizations, noise, or signal-to-noise ratio can vary widely across individuals, species, environment, and recording conditions, making it hard to develop robust and generalizable methods for ecological analyses. To account for these challenges, noise reduction techniques are used to remove noise before downstream analyses. Popular methods such as Noisereduce rely on spectral gating, which estimates a noise threshold for each frequency channel. We propose a further improvement to Noisereduce, leveraging the fact that most animal sounds have structure across multiple frequencies. We apply our method on bird and marine mammals recordings and show that our extension of Noisereduce leads to improved denoising in both above and under water acoustic recordings, without impacting speed of preprocessing.
Figures & tables
| Method | Seg-SNR ( ) | SI-SDR ( ) | MCD ( ) | LSD ( ) | |
|---|---|---|---|---|---|
| SNR 15 | Ours | 2.36 ∗ | 11.94 ∗ | 12.33 ∗ | 4.75 ∗ |
| Noisereduce | 1.48 | 11.65 | 14.29 | 5.01 | |
| Wiener | 1.61 | 10.87 | 17.93 | 5.33 | |
| Savitzky-Golay | -0.77 | 9.09 | 35.46 | 6.80 | |
| SNR 10 | Ours | 0.93 ∗ | 8.55 ∗ | 18.56 | 5.27 ∗ |
| Noisereduce | 8.13 | 20.13 | 5.79 |