A Fast One-Pass-Training Feature Selection Technique for GMM-based Acoustic Event Detection with Audio-Visual Data

Acoustic event detection becomes a difficult task, even for a small number of events, in scenarios where events are produced rather spontaneously and often overlap in time. In this work, we aim to improve the detection rate by means of feature selection. Using a one-against-all detection approach, a new fast one-pass-training algorithm, and an associated highly-precise metric are developed. Choosing a different subset of multimodal features for each acoustic event class, the results obtained from audiovisual data collected in the UPC multimodal room show an improvement in average detection rate with respect to using the whole set of features.