ALARM SOUND DETECTION USING TOPOLOGICAL SIGNAL PROCESSING

We present a novel approach to alarm sound detection using Topological Data Analysis. Our main focus is on proposing a new set of robust features, based on algebraic topology, that are aimed at capturing global structural information about the dynamical system underlying each input signal. In short, we convert each signal into a point cloud and compute its corresponding persistent homology, from which we can extract a variety of useful numerical features. We demonstrate the power of this framework using the UrbanSound8K dataset and show that, by combining topological features with a classical classification method, we achieve state-of-the-art results.