This project aims to identify the geographical position where an audio clip was recorded by analysing sound patterns and audio signals from the surrounding environment. This approach leverages hand-crafted and/or deep features to distinguish between different soundscapes associated with specific locations, like train stations, shopping malls, classrooms, hospitals, parks, and so on. Deep learning models are trained on labelled audio datasets that capture diverse environments and their unique acoustic signatures. By processing audio input, these systems can infer location based on the recognized sounds, such as traffic noise, voices, or ambient sounds. The project includes gathering datasets of audio clips with known geographical locations to train the machine learning models.