Campus Location Recognition using Audio Signals

People use sound both consciously and unconsciously to understand their surroundings. As we spend more time in a setting, whether in our car or our favorite cafe, we gain a sense of the soundscape the aggregate acoustic characteristics in the environment. Our project aims to test whether the acoustic environment in different areas of Stanford campus are distinct enough for a machine learning algorithm to localize a user based on the audio alone. We limit our localization efforts to seven distinct regions on Stanford campus as enumerated in Section III-C. We characterize the locations as “regions” because we hope to capture qualitative rather than quantitative descriptions. For example, the “Huang” region includes the outdoor patio area as well as the lawn beside the building. Furthermore, we restrict our efforts to daytime hours due to the significant soundscape differences between daytime and nighttime. A significant advantage of audio localization is the qualitative characterization on which we focus. Specifically, an acoustic environment does not generally linearly vary with position. For example, any point within a large room will likely have common acoustic characteristics. However, we expect a drastic soundscape change just outside the door or in another room, and that difference can be of significant value. However, GPS may not capture this change for two reasons: 1) This change may be below current GPS accuracy thresholds, typically 10-50 feet. 2) GPS only produces lat-long data. An additional layer of information is needed to provide information about the precise boundaries of the building. Furthermore, GPS fails to distinguish accurate vertical position (e.g. floors), which may be of special interest in buildings such as malls or department stores.