Indoor localization is a field in a development process. Different solutions have been introduced in recent years. Some of the solutions use beacons, WI-FI access points, different smartphone sensors, or acoustic sensing to make localizations. This paper is presenting an application that uses acoustic sensing data to perform localization with different deep models. The research aims to explore different models and evaluate their performance metrics in the classification of three different acoustic data sets and their overhead on the system. Two different architecture designs are implemented - a client-server one as the models are stored on the server and one only front-end oriented as in this case compressed models are used. The results show that the client-server approach outperforms the front-end only design as the former's models reach classification results of 98\%, 90\% and 90\% tested on three different data sets, despite taking longer to fetch a prediction result from the server compared to the compressed models stored on a smartphone device.