Variational autoencoders stabilise TCN performance when classifying weakly labelled bioacoustics data ...

• Main Authors: Fonollosa, Laia Garrobé, Gillespie, Douglas, Stankovic, Lina, Stankovic, Vladimir, Rendell, Luke
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Sound cs.SD; Audio and Speech Processing eess.AS; Quantitative Methods q-bio.QM; FOS: Computer and information sciences; FOS: Electrical engineering, electronic engineering, information engineering; FOS: Biological sciences; Physeter macrocephalus; Sperm whale
• Online Access: https://dx.doi.org/10.48550/arxiv.2410.17006; https://arxiv.org/abs/2410.17006

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 ...