In this paper we propose several methodologies for the use of feature integration and evaluate them in a low-latency classification framework. These general methodologies are based on three key aspects that will be assessed in this study: the selection of the features that have to be temporally integrated, the choice of the integration techniques, i.e., how the temporal information is extracted, and the size of the integration window. The experiments carried out for the speech/music/mix classification task show that the different methodologies have a significant impact on the global performance. Compared to the state of the art procedures, the methodologies we proposed achieved the best performance, even with the low-latency constraints.

Authors: Flocon-Cholet, Joachim; Faure, Julien; Guérin, Alexandre; Scalart, Pascal
Affiliations: Orange Labs, Lannion, France; INRIA/IRISA, Université de Rennes, Rennes, France(See document for exact affiliation information.)
AES Convention: 137 (October 2014) Paper Number: 9180
Publication Date: October 8, 2014
Subject: Signal Processing

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