ECHOv2: Two-Level Band-Splitting Representation Learning for Anomalous Sound Detection

Abstract

Machine anomalous sound detection (ASD) requires robust audio representations capable of capturing subtle deviations in machine sounds under limited supervision. Existing pre-trained audio backbones do not fully capture frequency-specific characteristics of machine sounds. To address this, we propose ECHOv2, a band-splitting model that learns localized intra-band representations to capture fine-grained spectral patterns while also incorporating a two-level self-distillation strategy with explicit inter-band supervision to model cross-frequency dependencies. The inter-band branch performs global context alignment and masked sub-band reconstruction, and multiple summary tokens are introduced for structured aggregation with controllable frequency granularity, enabling region-aware interaction across sub-bands during training. This design allows ECHOv2 to robustly handle diverse machine types and noisy operating conditions while maintaining stable representation quality. To enable fair and consistent evaluation of pre-trained audio backbones, we establish a unified ASD benchmark over DCASE 2020-2025 with two complementary protocols: embedding-based evaluation for frozen representation discriminability and adaptation-based evaluation for downstream transferability. Ablation studies confirm the effectiveness of intra-band learning, inter-band supervision, and structured aggregation granularity for robust ASD representation learning. These findings demonstrate that structured cross-band modeling provides a powerful and adaptable framework for ASD representation learning and can serve as a strong foundation for future research. The model and benchmark are fully open-sourced at https://github.com/yucongzh/ECHOv2 and https://github.com/yucongzh/ASD_Benchmark to promote reproducible research.