{"slug": "effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat", "title": "Effects of interpulse-interval variation on deep-learning classification of bat vocalizations", "summary": "A study posted to arXiv (2610.02284v1) found limited support for the hypothesis that natural interpulse-interval (IPI) variation contributes substantially to automated bat-species classification, with transformer model PaSST reaching 71 ± 2.3% accuracy under natural IPI versus 70 ± 6.3% under normalized 50-ms IPI, while EfficientNet-B0 rose from 47 ± 4.7% to 57 ± 3.9%. In cross-condition tests, models trained on natural-IPI recordings outperformed those trained on normalized-IPI recordings on the natural-IPI test set, with accuracy falling from 54% to 50% for EfficientNet and from 65% to 57% for PaSST. The pretrained classifiers BatDetect2 and BAT differed little between IPI conditions, and the authors conclude results obtained under normalized conditions may not transfer fully to natural recordings.", "body_md": "arXiv:2610.02284v1 Announce Type: new \nAbstract: Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variation in the interpulse interval (IPI)-the time between consecutive call onsets-provides species-discriminative information and whether transformer-based models are more sensitive to this information than convolutional neural networks. We created two matched datasets from European bat recordings: a natural-IPI condition retaining the original call timing and a normalized-IPI condition in which call onsets were spaced at 50-ms intervals. EfficientNet-B0 and PaSST were fine-tuned and evaluated within each condition. In an additional experiment, each architecture was trained separately on natural-IPI and normalized-IPI recordings, and evaluated on the same natural-IPI test set. Finally, the pretrained classifiers BatDetect2 and BAT were evaluated on both conditions. Within-condition IPI normalization had model-dependent effects. PaSST accuracy differed little between the natural-IPI ($71 \\pm 2.3\\%$) and normalized-IPI ($70 \\pm 6.3\\%$) conditions, whereas EfficientNet accuracy increased from $47 \\pm 4.7\\%$ to $57 \\pm 3.9\\%$. PaSST exceeded EfficientNet under both conditions. In the cross-condition evaluation, models trained on natural-IPI recordings outperformed those trained on normalized-IPI recordings on the natural-IPI test set: accuracy decreased from 54% to 50% for EfficientNet and from 65% to 57% for PaSST. BatDetect2 and BAT differed little between IPI conditions. Overall, we found limited support for the hypotheses that natural IPI variation contributes substantially to bat-species classification and that it is used more effectively by transformer-based than CNN-based models. Nevertheless, the cross-condition performance decrease shows that results obtained under normalized conditions may not transfer fully to natural recordings.", "url": "https://wpnews.pro/news/effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat", "canonical_source": "https://arxiv.org/abs/2610.02284", "published_at": "2026-10-05 04:00:00+00:00", "updated_at": "2026-10-05 04:12:37.495335+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks"], "entities": ["arXiv", "EfficientNet-B0", "PaSST", "BatDetect2", "BAT"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat", "markdown": "https://wpnews.pro/news/effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat.md", "text": "https://wpnews.pro/news/effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat.txt", "jsonld": "https://wpnews.pro/news/effects-of-interpulse-interval-variation-on-deep-learning-classification-of-bat.jsonld"}}