Ambient Sound Analysis for Non-Invasive Indoor Activity Detection in Edge Computing Environments

Research on detecting the behavior of residents using sounds generated in living spaces has been conducted by sending the sound data to a server or cloud and utilizing a relatively large artificial intelligence model. However, this method generates excessive data traffic and carries a privacy risk by transmitting sounds unnecessary for behavior detection. In this paper, we explored data processing methods suitable for a non-invasive indoor noisy sound analysis system operating in an edge environment. To achieve this goal, we implemented Mel-spectrogram and Mel-Frequency Cepstral Coefficients (MFCC) based models for classifying environmental sounds, comparing their performance based on different preprocessing parameters and optimizations. Furthermore, we evaluated the computational resource usage and performance of the models in both the Raspberry Pi and microcontroller environments.