DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment
This provides a specialized dataset for audio classification in dementia care, though it is incremental as it builds on existing database creation methods.
The authors tackled the lack of detailed synthetic domestic audio databases by creating an 11-class database with clean and noisy signals, emulating dementia patient environments, and achieved a weighted F1-score of 86.24% using a baseline model.
Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact location of the sound sources, and the associated noise levels. In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. Data is carefully curated such that it reflects issues commonly faced in a dementia patients environment, and recreate scenarios that could occur in real-world settings. Similarly, the room impulse response generated is based on a typical one-bedroom apartment at Hebrew SeniorLife Facility. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz. Using our baseline model using Continues Wavelet Transform Scalograms and AlexNet, this yielded a weighted F1-score of 86.24 percent.