{ "abstract": "
ul.contains-task-list]:pl-0 [&>ol.contains-task-list]:pl-0 [&_ul]:pl-10 [&_ol]:pl-10 [&>ul:not(.contains-task-list)]:!pl-4 [&>ol:not(.contains-task-list)]:!pl-4 [&_ul]:list-disc [&_ol]:list-decimal [&_li.task-checked>.task-checked-content]:opacity-50 md-divider-spacing break-words gap-2\"> This preprint evaluates direct transfer, supervised target adaptation, and validation protocol design of convolutional neural networks for acoustic water leak detection using field recordings from SebaKMT correlation loggers. A single-scale CNN (25,633 parameters) is compared with a three-branch multiscale time–frequency network (TFCNN, 76,769 parameters) and classical machine learning baselines across multiple experimental protocols. In direct transfer from an unstructured multi-instrument collection, multiscale TFCNN increased sensitivity from 78.20% to 86.40% compared to the single-scale network, but exhibited low specificity (16.50%). While supervised adaptation of the output classifier improved test score ranking (ROC-AUC 94.16%), threshold selection subject to high validation recall retained 20 alarms among 40 negative field recordings. When trained from scratch on 180 SebaKMT recordings, random file partitioning yielded 100% accuracy in all ten runs, which is shown to be inflated by shared recording sessions between training and test sets. Under site-disjoint grouped cross-validation across six registered site groups, TFCNN out-of-fold accuracy was 87.83 ± 3.22% (mean within-fold ROC-AUC 94.28 ± 3.87%). A classical logistic regression baseline using nine handcrafted signal descriptors without digital amplitude achieved an accuracy similar to TFCNN (87.78% vs. 87.83%), with higher sensitivity (98.33% vs. 92.67%) and lower specificity (82.50% vs. 85.42%), demonstrating no detectable accuracy advantage for the deep architecture on this corpus. Furthermore, logistic regression on digital RMS alone reached 94.44% accuracy at threshold 0.50 (within-fold ROC-AUC 99.67 ± 0.58%), although its sensitivity was lower at 83.33%. This strong predictive association motivates investigation of acquisition-related shortcuts rather than confirming a physical acoustic mechanism. The study concludes that acoustic diagnostics on small field datasets require strict site-disjoint validation protocols and transparent classical feature baselines alongside deep neural networks to prevent session leakage and shortcut learning.", "abstract_shown": true, "affiliations": [ "Rivixi LLC" ], "article_type": null, "arxiv": null, "author_total": 3, "authors": [ { "affs": [ 0 ], "name": "Aleksandr Ivanaiskii", "orcid": "0009-0009-2318-1194" }, { "affs": [ 0 ], "name": "Evgeny Ivanaiskii", "orcid": "0009-0005-6978-5170" }, { "affs": [ 0 ], "name": "Sergei Shipilov", "orcid": "0009-0009-5554-5183" } ], "bibtex": "https://research.draconitic.com/paper/b4fkm6s6zmmea.bib", "cited_by": 0, "doi": "10.5281/zenodo.23446900", "fulltext_url": null, "id": "b4fkm6s6zmmea", "issns": [], "landing_url": "https://zenodo.org/records/23446900", "language": "eng", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [], "openalex": null, "pdf_url": "https://zenodo.org/api/records/23446900/files/sebakmt-transfer-validation.pdf/content", "pmcid": null, "pmid": null, "published": "2026-10-18", "publisher": null, "ris": "https://research.draconitic.com/paper/b4fkm6s6zmmea.ris", "sources": [ { "first_seen": "2026-11-01T12:21:45Z", "native_id": "23446900", "source": "zenodo", "url": "https://zenodo.org/records/23446900" } ], "subjects": [], "title": "Evaluating Transferability and Validation Protocols of Convolutional Neural Networks for Acoustic Water Leak Detection Using SebaKMT Logger Recordings", "title_text": "Evaluating Transferability and Validation Protocols of Convolutional Neural Networks for Acoustic Water Leak Detection Using SebaKMT Logger Recordings", "type": "preprint", "updated_at": "2026-11-01T12:21:45Z", "url": "https://research.draconitic.com/paper/b4fkm6s6zmmea", "venue": "Zenodo", "versions": [] }