{"ID":2880256,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14689","arxiv_id":"2508.14689","title":"ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals","abstract":"Pre-trained foundation models have demonstrated remarkable success in audio, vision and language, yet their potential for general machine signal modeling with arbitrary sampling rates-covering acoustic, vibration, and other industrial sensor data-remains under-explored. In this work, we propose a novel foundation model ECHO that integrates an advanced band-split architecture with frequency positional embeddings, enabling spectral localization across arbitrary sampling configurations. Moreover, the model incorporates sliding patches to support inputs of variable length without padding or cropping, producing a concise embedding that retains both temporal and spectral fidelity and naturally extends to streaming scenarios. We evaluate our method on various kinds of machine signal datasets, including previous DCASE task 2 challenges (2020-2025), and widely-used industrial signal corpora. Experimental results demonstrate consistent state-of-the-art performance in machine signal anomaly detection and fault classification, confirming the effectiveness and generalization capability of the proposed model. We open-sourced ECHO on https://github.com/yucongzh/ECHO.","short_abstract":"Pre-trained foundation models have demonstrated remarkable success in audio, vision and language, yet their potential for general machine signal modeling with arbitrary sampling rates-covering acoustic, vibration, and other industrial sensor data-remains under-explored. In this work, we propose a novel foundation model...","url_abs":"https://arxiv.org/abs/2508.14689","url_pdf":"https://arxiv.org/pdf/2508.14689v4","authors":"[\"Yucong Zhang\",\"Juan Liu\",\"Ming Li\"]","published":"2025-08-20T13:10:44Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false,"code_links":[{"ID":610662,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2880256,"paper_url":"https://arxiv.org/abs/2508.14689","paper_title":"ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals","repo_url":"https://github.com/yucongzh/ECHO","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}