Characterising and detecting fin whale calls using deep learning at the Lofoten-Vesterålen Observatory, Norway

Abstract

The application of deep learning to solving acoustic detection and identification challenges is a rapidly-evolving subfield of underwater acoustics. Automatic signal identification can be used for many applications, like enabling the compilation of large datasets from many sources, which can be used to better constrain source-specific characteristics and trends. Earlier analyses (Garibbo et al., 2020) identified the different contributions of wind, weather, shipping and earthquakes. The long-term acoustic measurements regularly include calls from fin whales, whose presence and vocal activities in the area vary with seasons; their 20-Hz calls are sometimes mixed with other signals, like earthquakes or shipping. We present here the application of deep learning to automatically identify these whale calls. Percentile analyses of the temporal variation of the frequency of calls, their Power Spectral Density (PSD), and Sound Pressure Level (SPL) is carried out to determine their respective contributions to the overall soundscape and highlight relevant information about these whale populations. The deep learning approaches selected here can also be used for other types of animal vocalisations and for other short-term processes (e.g. passing ships, earthquakes of different types), assisting in their identification and in the statistical and temporal analyses of low-frequency soundscapes.