Convolutional Neural Networks for audio classification on ultra low power IoT devices
Alessandro Andreadis, Giovanni Giambene, Riccardo Zambon · 2021
Sound classification usually requires heavy resources in terms of computation, memory, and energy to achieve good accuracy. However, it is possible to enable a more efficient and accurate audio recognition on pervasive IoT platforms through specific optimizations. In this paper, a solution based on convolutional neural networks is proposed for audio classification on resource-constrained wireless edge devices. Furthermore, different pre-processing techniques have been tested to evaluate the classification accuracy with respect to computational, memory, and energy footprint.