Abstract
Scene recognition of audiologically relevant environments is important for hearing aids; however, it is challenging, in part because of the limitations of existing datasets. Datasets often lack public accessibility, completeness, or audiologically relevant labels, hindering systematic comparison of machine learning models. Deploying these models on resource-constrained edge devices presents another challenge. Our solution is two-fold: we leverage several open source datasets to create AHEAD-DS, a dataset designed for scene recognition of audiologically relevant environments, and introduce YAMNet+, a sound recognition model. AHEAD-DS aims to provide a standardised, publicly available dataset with consistent labels relevant to hearing aids, facilitating model comparison. YAMNet+ is designed for deployment on edge devices like smartphones connected to hearing devices, such as hearing aids and wireless earphones with hearing aid functionality; serving as a baseline model for sound-based scene recognition. YAMNet+ achieved a mean average precision of 0.83 and accuracy of 0.93 on the testing set of AHEAD-DS across fourteen categories of audiologically relevant environments. We found that applying transfer learning from the pretrained YAMNet model was essential. We demonstrated real-time sound-based scene recognition capabilities on edge devices by deploying YAMNet+ to an Android smartphone. Even with a Google Pixel 3 (a phone with modest specifications, released in 2018), the model processes audio with approximately 50ms of latency to load the model, and an approximate linear increase of 30ms per 1 second of audio. Our website and code this https URL .
Abstract (translated)
听力相关环境的场景识别对于助听设备来说非常重要，但由于现有数据集存在的一些限制（如缺乏公共访问性、完整性或与听力相关的标签），这方面的研究面临不少挑战。这些不足阻碍了机器学习模型之间的系统比较和评估。此外，在资源受限的边缘设备上部署这些模型也是一项挑战。 我们的解决方案分为两个部分：首先，我们利用几个开源数据集创建了一个名为AHEAD-DS的数据集，该数据集专为听力相关的环境场景识别设计，并且提供标准化、公开可用并带有与助听器相关的一致标签。其次，我们引入了YAMNet+模型，这是一种旨在部署在诸如智能手机等边缘设备上的声音识别模型，这些设备连接着如助听器和具备助听功能的无线耳机等装置，用作基于声音场景识别的基础模型。 AHEAD-DS数据集包含了十四类听力相关的环境类别，并为它们提供了标准标签，使得不同的机器学习模型能够进行有效比较。我们的YAMNet+模型在测试集上达到了平均精度（mean average precision）0.83和准确率（accuracy）0.93的成绩。 我们发现从预训练的YAMNet模型中应用迁移学习是十分必要的，并且我们在Android手机上部署了YAMNet+，展示了其实时声音场景识别的能力。即使是在2018年发布的Google Pixel 3这样的设备上（性能较为一般），该模型也能以大约50毫秒的时间加载并运行，并且每处理一秒钟的音频只增加了约30毫秒的时间。 我们的研究网站和代码可以在 [这里](#) 查看。（注意：原文中提供的网址被省略了，请您根据实际情况填写具体链接。）
URL
https://arxiv.org/abs/2508.10360
