This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark comprehensive methods, including (i) fully supervised deep learning, (ii) embedding-based classification, (iii) supervised foundation model fine-tuning, and (iv) self-supervised model fine-tuning. Our best strategy achieves nearly 90% classification accuracy by fine-tuning all parameters of a self-supervised model. These results highlight acoustic scattering as a privacy-preserving, non-contact alternative to visual classification, opening huge potential for applications in various industries.

翻译：本文提出了一种利用声散射进行非侵入式物体分类的新方法，并以头发评估为案例进行演示。当入射波与物体相互作用时，会产生编码结构及材料特性的散射声场。通过发射声刺激并捕获来自带有头发样本的头部物体的散射信号，我们利用基于人工智能、深度学习的声学分类方法对头发类型和湿度进行分类。我们对多种方法进行了基准测试，包括：(i) 全监督深度学习；(ii) 基于嵌入的分类；(iii) 监督式基础模型微调；以及 (iv) 自监督模型微调。我们最佳的策略是通过微调自监督模型的所有参数，实现了近90%的分类准确率。这些结果凸显了声散射作为一种保护隐私、非接触式的视觉分类替代方案，为各行业的应用开辟了巨大潜力。