Exploring energy-efficient key word spotting on Android using network compression techniques and a model selection algorithmŠTIMEC, GAŠPER (Avtor) Machidon, Octavian Mihai (Mentor) keyword spottingneural networkMFCCThis thesis explores the development of an Android application for real-time keyword spotting, utilizing neural network based models deployed locally on the device. A central aspect of this work is the implementation of an algorithm that selects the most appropriate level of model complexity based on internal factors such as the device’s battery level and previous inference outcomes. Three models used were created using varying architecture designs and post-training quantization. The application captures audio through the device’s microphone, processes it to extract MFCC features, selects the model and performs classification, ensuring that the processing pipeline remains consistent with the model’s training conditions. Beyond the application itself, this thesis aims to provide insights into the performance of the adaptive complexity algorithm. It evaluates the trade-offs between energy consumption and classification accuracy across different app configurations: using a single model versus employing the adaptive algorithm.20242024-11-13 12:13:27Diplomsko delo/naloga164830VisID: 37455COBISS_ID: 214137859sl