ECHO模型实现任意采样率信号的精准频谱定位与变长输入处理。
ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signals
- 分频带架构+频率位置编码，支持任意采样率下的频谱定位
- 滑动块设计免去填充裁剪，保持时频保真度
- 适用于工业传感器异常检测，适合流式信号场景
预训练基础模型在音频、视觉和语言领域表现卓越，但在覆盖声学、振动及其他工业传感器数据的任意采样率通用信号建模方面潜力尚未充分挖掘。本文提出新型基础模型ECHO，融合先进分频带架构与频率位置嵌入，实现任意采样配置下的频谱定位。模型引入滑动块机制，支持变长输入而无需填充或裁剪，生成兼具时序与频谱保真度的紧凑嵌入，并自然延伸至流式场景。我们在多种机器信号数据集上评估该方法，包括2020-2025年连续的DCASE任务2挑战数据集及广泛使用的工业信号语料库。实验结果表明，在机器信号异常检测与故障分类任务中均达到一致领先的性能，验证了模型的有效性与泛化能力。代码已开源：https://github.com/yucongzh/ECHO。
原文摘要 · Abstract (English)
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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