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arXiv Machine Learning By Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier, Dan Stowell

Effects of interpulse-interval variation on deep-learning classification of bat vocalizations

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The study examined how variation in the interpulse interval (IPI) of bat vocalizations affects deep‑learning classification. Two datasets—one preserving natural IPI timing and another normalizing call spacing to 50 ms—were used to fine‑tune EfficientNet‑B0 and PaSST models. Results showed that IPI normalization had mixed effects: PaSST performance remained stable while EfficientNet improved, yet models trained on natural IPI data performed better on natural test sets, indicating limited but non‑negligible influence of natural IPI variation.

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