ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals

• URL: http://arxiv.org/abs/2508.14689v3
• Date: Sat, 27 Sep 2025 10:05:10 GMT
• Title: ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals
• Authors: Yucong Zhang, Juan Liu, Ming Li,
• Abstract summary: We propose a novel foundation model ECHO that integrates an advanced band-split architecture with frequency positional embeddings.<n>We evaluate our method on various kinds of machine signal datasets.
• Score: 8.411477071838592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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