仿人耳反馈机制，让机器听觉更适应复杂环境。
BiEAR: A Human Auditory-Inspired Adaptive Binaural Front-end for Multi-Speaker Localisation and Distance Estimation
- 用神经控制器动态调整双耳滤波器的频率选择性
- 在真实房间中定位准确率提升，对陌生说话人更鲁棒
- 可解释性强，聚焦关键频段，适合复杂声学场景
我们提出BiEAR，一种受人类听觉系统中内侧橄榄耳蜗反馈（MOC）启发的自适应双耳前端，用于多说话人定位与距离估计。BiEAR在推理过程中通过神经控制器动态调节双耳滤波器组的频率选择性，生成时频自适应的听觉表征，使模型能响应变化的声学条件。我们在无混响和真实房间环境中评估了该方法，结果表明，相较于常用的固定双耳前端，自适应前端显著提升了定位精度，并增强了对未见说话人和房间的鲁棒性。可视化分析显示，模型会随时间强调具有信息量的频段。这些发现表明，生物启发的自适应双耳前端可有效提升机器听觉在复杂声学场景中的鲁棒性。
原文摘要 · Abstract (English)
We present BiEAR, a human auditory-inspired adaptive binaural front-end for multi-speaker localisation and distance estimation. Inspired by medial olivocochlear (MOC) feedback in human hearing, BiEAR uses a neural controller to adaptively adjust the frequency selectivity of a binaural auditory filterbank during inference. This yields time-frequency adaptive representations for ears, enabling the model to respond to changing acoustic conditions. We evaluate BiEAR on multi-speaker localisation and distance estimation in anechoic and real-room environments. Results show that the adaptive front-end improves localisation accuracy and robustness to unseen speakers and rooms compared with commonly used fixed binaural front-ends. Visualisation and analysis of learned filter adaptations show that BiEAR emphasises informative frequency bands over time. These findings suggest that adaptive, biologically inspired binaural front-ends can improve machine hearing robustness in complex acoustic scenes.
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