  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.
