Abstract:
Automatic sound understanding tasks have been very popular within research community during the last years. The success of deep learning data driven applications in many signal understanding fields is now moving from centralized cloud services to the edge of the network, close to the nodes where raw data are generated from different type of sensors. In this paper we show a complete workflow for a context awareness acoustic scene classification (ASC) application and its effective embedding process into an ultra-low power microcontroller (MCU). It can widen the capabilities of edge AI applications, from environmental and inertial sensors up to acoustic signals, which require more bandwidth and generate more data. In the paper the entire workflow of such development is described in terms of dataset collection, selection and annotations, acoustic features representation, neural net modeling and optimization as well as the efficient embedding step of the whole application into the target low power 32-bit microcontroller device. Moreover, the overall accuracy of the proposed model and the capability to be real time executed together with an audio feature extraction process shows that such kind of audio understanding application can be efficiently deployed on power constrained battery-operated devices.
