Abstract
Nowadays, generalized sound recognition technology is constantly gaining attention within the generic context of scene analysis and understanding (smart-home, surveillance, bioacoustics, etc.). It is typically achieved using a set of relevant to the task at hand descriptors modelled by means of a statistical tool, e.g., hidden Markov model. This work exhaustively applies the Universal Modeling (UM) (or class-independent) approach on the particular task. The feature extraction engine extracts descriptors belonging to time, frequency and wavelet domains. We describe a novel data selection scheme based on Gaussian mixture model clustering for the creation of the UM. The scheme takes into account the dataset characteristics, adapts itself to them and leads to higher recognition rates than the standard UM approach.
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