An investigation of temporal feature integration for a low-latency classification with application to speech/music/mix classification

Joachim Flocon-Cholet (1) , Alexandre Guérin (2) , Julien Faure (1) , Pascal Scalart (3)

1 Orange Labs [Lannion]
2 Orange Labs [Cesson-Sévigné]
3 CAIRN - Energy Efficient Computing ArchItectures with Embedded Reconfigurable Resources

In this study, 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 which 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 good performance, even with the low-latency constraints.

Mots clés: Temporal Integration; SVM Support Vector Machines; Audio classification

Domaines: Traitement du signal et de l'image [eess.SP]

Joachim Flocon-Cholet, Alexandre Guérin, Julien Faure, Pascal Scalart. An investigation of temporal feature integration for a low-latency classification with application to speech/music/mix classification. 137th Audio Engineering Society Convention, no 9180, AES, Oct 2014, Los Angeles, United States. ⟨hal-01100261⟩