Recurrence quantification analysis features for environmental sound recognition

This paper tackles the problem of feature aggregation for recognition of auditory scenes in unlabeled audio. We describe a new set of descriptors based on Recurrence Quantification Analysis (RQA), which can be extracted from the similarity matrix of a time series of audio descriptors. We analyze their usefulness for environmental audio recognition combined with traditional feature statistics in the context of the AASP D-CASE[1] challenge. Our results show the potential of non-linear time series analysis techniques for dealing with environmental sounds.