  Research related to automatically detecting Alzheimer's disease (AD) is
important, given the high prevalence of AD and the high cost of traditional
methods. Since AD significantly affects the acoustics of spontaneous speech,
speech processing and machine learning (ML) provide promising techniques for
reliably detecting AD. However, speech audio may be affected by different types
of background noise and it is important to understand how the noise influences
the accuracy of ML models detecting AD from speech. In this paper, we study the
effect of fifteen types of environmental noise from five different categories
on the performance of four ML models trained with three types of acoustic
representations. We perform a thorough analysis showing how ML models and
acoustic features are affected by different types of acoustic noise. We show
that acoustic noise is not necessarily harmful - certain types of noise are
beneficial for AD detection models and help increasing accuracy by up to 4.8\%.
We provide recommendations on how to utilize acoustic noise in order to achieve
the best performance results with the ML models deployed in real world.
