  This paper elucidates a model for acoustic single and multi-tone
classification in resource constrained edge devices. The proposed model is of
State-of-the-art Fast Accurate Stable Tiny Gated Recurrent Neural Network. This
model has resulted in improved performance metrics and lower size compared to
previous hypothesized methods by using lesser parameters with higher efficiency
and employment of a noise reduction algorithm. The model is implemented as an
acoustic AI module, focused for the application of sound identification,
localization, and deployment on AI systems like that of an autonomous car.
Further, the inclusion of localization techniques carries the potential of
adding a new dimension to the multi-tone classifiers present in autonomous
vehicles, as its demand increases in urban cities and developing countries in
the future.
