The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection
This dataset supports sound event detection research for promoting wellbeing in older adults, but it is incremental as it focuses on a new dataset with privacy features rather than novel methods.
The paper introduces a residential audio dataset for sound event detection in smart homes, created by recording 8 older adults' homes for 7 days and using an automated speech removal pipeline to ensure privacy, resulting in privacy-compliant recordings that capture daily living soundscapes.
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.