Automatic noise reduction of extremely sparse vocalisations for bioacoustic monitoring

Abstract

Environmental noise and data sparsity present major challenges within the field of bioacoustics. The presence of noise degrades the analysis of audio and field recordings commonly containing large quantities of data with sparse vocalisation features. This work explores noise reduction (audio enhancement) techniques in the context of extremely sparse vocalisations (< 1% occurrence rates) of invasive mammalian and marsupial species, and the clear implications for other bioacoustics applications which face similar challenges. This work compares relevant noise reduction techniques and recommends a spectral subtraction approach. Spectral subtraction achieved a 42.1 dB improvement in signal to noise power (SnNR) and a 2.7 dB improvement in noise variance (SR). We also demonstrate reliable noise reduction at bandwidths up to 250 kHz and efficiency improvements compared to alternative methods. We explore the benefits of deep audio enhancement approaches demonstrating comparable noise reduction with improvements in transient noise reduction but also key limitations such as bandwidth, efficiency and data generation in bioacoustics applications. We identify how the contributions of this work can be applied within the broader context of bioacoustics. All data and code is publicly available at https://github.com/BenMcEwen1/Sparse-Noise-Reduction