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Variational autoencoders stabilise TCN performance when classifying weakly labelled bioacoustics data by Fonollosa, Laia Garrobé; Gillespie, Douglas; Stankovic, Lina; Stankovic, Vladimir; Rendell, Luke is a scholarly article available to read on EtoBox.

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Passive acoustic monitoring (PAM) data is often weakly labelled, audited at the scale of detection presence or absence on timescales of minutes to hours. Moreover, this data exhibits great variability from one deployment to the next, due to differences in ambient noise and the signals across sources and geographies. This study proposes a two-step solution to leverage weakly annotated data for training Deep Learning (DL) detection models. Our case study involves binary classification of the presence/absence of sperm whale (\textit{Physeter macrocephalus}) click trains in 4-minute-long recordings from a dataset comprising diverse sources and deployment conditions to maximise generalisability. We tested methods for extracting acoustic features from lengthy audio segments and integrated Temporal Convolutional Networks (TCNs) trained on the extracted features for sequence classification. For feature extraction, we introduced a new approach using Variational AutoEncoders (VAEs) to extract information from both waveforms and spectrograms, which eliminates the necessity for manual threshold setting or time-consuming strong labelling. For classification, TCNs were trained separately on sequ

Author

Fonollosa, Laia Garrobé; Gillespie, Douglas; Stankovic, Lina; Stankovic, Vladimir; Rendell, Luke

Published

2024

Language

EN