基于无标注适应与预训练模型迁移的四足机器人听觉开集自身噪声分离
Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种无标注适应的开集自身噪声分离框架，利用RecurGraph选择噪声片段、Transfer-DiT迁移预训练模型，在双足和四足机器人上验证了分离性能提升。
AI中文摘要：
本文提出了一种面向四足机器人听觉的开集自身噪声分离框架，该框架通过无标注适应和预训练模型迁移实现。该框架在去除机器人自身噪声的同时，保留类别未预先指定的环境声音。声学感知能提供超出视觉范围的机器人周围环境线索，但行走引起的自身噪声，包括脚步冲击、关节间隙咔嗒声和电机噪声，严重污染了录音。该框架首先使用RecurGraph，通过将片段嵌入聚合成嵌入质心并在音频嵌入图上传播分数，从未标注录音中选择以自身噪声为主的片段。所选片段与来自大规模声音事件数据集的各种环境声音混合，为开集分离提供配对的混合-目标监督。随后，Transfer-DiT对通用的零样本神经分离器进行适配，以实现目标机器人的高保真开集自身噪声分离。使用双足和四足机器人的实验表明，片段选择可靠，且分离质量和下游任务性能均优于基线分离器。这些结果证明了在无需单独记录的纯自身噪声数据或人工片段级标注的情况下，无标注适应的可行性。
英文摘要：
This paper proposes an open-set ego-noise separation framework for legged-robot audition via annotation-free adaptation and pretrained-model transfer. The framework removes robot-specific ego-noise while preserving environmental sounds whose classes are not specified in advance. Acoustic sensing provides cues about a robot's surroundings beyond the visual field, but walking-induced ego-noise from footstep impacts, joint-backlash rattling, and motor noise severely contaminates the recordings. The framework first uses RecurGraph to select ego-noise-dominant clips from the unlabeled recordings by aggregating clip embeddings into an embedding centroid and propagating scores over an audio-embedding graph. The selected clips are mixed with diverse environmental sounds from a large-scale sound-event dataset to provide paired mixture--target supervision for open-set separation. Transfer-DiT then adapts a general-purpose zero-shot neural separator to achieve high-fidelity open-set ego-noise separation for the target robot. Experiments with bipedal and quadrupedal robots show reliable clip selection and improvements in separation quality and downstream task performance over baseline separators. These results demonstrate the feasibility of annotation-free adaptation without separately recorded ego-noise-only data or manual clip-level annotations.
