Domain-adaptation method between acoustic-response data using different insert earphones.

Kiyean Kim, Sangyeon Kim, Sukkyu Sun
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Abstract

Classifying acoustic responses captured through earphones offers valuable insights into nearby environments, such as whether the earphones are in or out of the ear. However, the performances of classification algorithms often suffer when applied to other devices due to domain mismatches. This study proposes a domain-adaptation method tailored for acoustic-response data from two distinct insert earphone models. The method trains a domain-adaptation function using a pair of datasets obtained from a set of acoustic loads, yielding a domain-adapted dataset suitable for training classification algorithms in a target domain. The effectiveness of this approach is validated through assessments of domain adaptation quality and resulting performance enhancements in the classification algorithm tasked with discerning whether an earphone is positioned inside or outside the ear. Importantly, our method requires significantly fewer measurements than the original dataset, reducing data collection time while providing a suitable training dataset for the target domain. Additionally, the method's reusability across future devices streamlines data collection time and efforts for the future devices.
使用不同插入式耳机的声学响应数据之间的领域适应方法。
对通过耳机捕捉到的声学响应进行分类,可为了解附近环境提供有价值的信息,例如耳机是在耳内还是耳外。然而,当分类算法应用于其他设备时,由于领域不匹配,其性能往往会受到影响。本研究针对两种不同插入式耳机模型的声学响应数据提出了一种领域适应方法。该方法使用从一组声学负载中获得的一对数据集来训练领域适应函数,从而获得适合在目标领域中训练分类算法的领域适应数据集。通过对领域适应质量的评估,以及对负责辨别耳机是位于耳内还是耳外的分类算法的性能提升,验证了这种方法的有效性。重要的是,与原始数据集相比,我们的方法所需的测量数据要少得多,从而减少了数据收集时间,同时为目标领域提供了合适的训练数据集。此外,该方法在未来设备上的可重用性简化了未来设备的数据收集时间和工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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