The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection

Gabriel Bibbó, Thomas Deacon, Arshdeep Singh, Mark D. Plumbley
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Abstract

This paper presents a residential audio dataset to support sound event detection research for smart home applications aimed at promoting wellbeing for older adults. The dataset is constructed by deploying audio recording systems in the homes of 8 participants aged 55-80 years for a 7-day period. Acoustic characteristics are documented through detailed floor plans and construction material information to enable replication of the recording environments for AI model deployment. A novel automated speech removal pipeline is developed, using pre-trained audio neural networks to detect and remove segments containing spoken voice, while preserving segments containing other sound events. The resulting dataset consists of privacy-compliant audio recordings that accurately capture the soundscapes and activities of daily living within residential spaces. The paper details the dataset creation methodology, the speech removal pipeline utilizing cascaded model architectures, and an analysis of the vocal label distribution to validate the speech removal process. This dataset enables the development and benchmarking of sound event detection models tailored specifically for in-home applications.
家的声音用于声音事件检测的语音去除住宅音频数据集
本文介绍了一个住宅音频数据集,用于支持智能家居应用的声音事件检测研究,旨在提高老年人的健康水平。该数据集是通过在 8 位 55-80 岁的参与者家中部署音频录音系统构建的,为期 7 天。通过详细的平面图和建筑材料信息记录了声学特征,以便为人工智能模型的部署复制录音环境。利用预先训练的音频神经网络,开发了一种新型的自动语音移除管道,用于检测和移除包含语音的片段,同时保留包含其他声音事件的片段。由此产生的数据集由符合隐私标准的音频记录组成,准确捕捉了住宅空间内的声音景观和日常生活活动。论文详细介绍了数据集创建方法、利用级联模型架构的语音移除管道,以及对人声标签分布的分析,以验证语音移除过程。通过该数据集,可以开发专门针对家庭应用的声音事件检测模型,并对其进行基准测试。
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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