Environmental audio scene and activity recognition through mobile-based crowdsourcing

Environmental audio recognition through mobile devices is difficult because of background noise, unseen audio events, and changes in audio channel characteristics due to the phone´s context, e.g., whether the phone is in the user´s pocket or in his hand. We propose a crowdsourcing framework that models the combination of scene, event, and phone context to overcome these issues. The framework gathers audio data from many people and shares user-generated models through a cloud server to accurately classify unseen audio data. A Gaussian histogram is used to represent an audio clip with a small number of parameters, and a k-nearest classifier allows the easy incorporation of new training data into the system. Using the Kullback-Leibler divergence between two Gaussian histograms as the distance measure, we find that audio scenes, events, and phone context are classified with 85.2%, 77.6%, and 88.9% accuracy, respectively.