ECHOv2
ECHOv2 is a band-splitting audio representation model for anomalous sound detection (ASD) with explicit cross-band interaction. It extends the original ECHO framework (link) with a two-level self-distillation design to model both intra-band and inter-band information.
✨ Key features
Band-splitting modeling: learns frequency-localized representations from spectrogram sub-bands.
Cross-band interaction: explicitly models dependencies across frequency regions.
Two-level self-distillation: jointly optimizes intra-band and inter-band learning.
Structured summary tokens: supports region-aware cross-band aggregation with controllable granularity.
Strong ASD performance: achieves consistent gains on DCASE 2020–2025 ASD benchmarks.
📊 Performance
The overall performance on the ASD benchmarks.
Embedding-based Results:
Adaptation-based Results:
Minimal usage
import torchaudio
from ECHOv2 import from_pretrained
model = from_pretrained()
wav, sr = torchaudio.load("/path/to/audio.wav")
if wav.shape[0] > 1:
wav = wav.mean(dim=0, keepdim=True)
utt_feature, frame_feature = model.extract_features_from_audio(wav, sr)
print(utt_feature.shape, frame_feature.shape)
📚 Citation
@inproceedings{echo2025,
title={ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signal},
author={Yucong Zhang and Juan Liu and Ming Li},
booktitle={Proc. ICASSP},
year={2026},
}