Sound Representation and Classification Benchmark for Domestic Robots

Maxime Janvier 1 Xavier Alameda-Pineda 1 Laurent Girin 2 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-MAGIC - MAGIC
GIPSA-DPC - Département Parole et Cognition
Abstract : We address the problem of sound representation and classification and present results of a comparative study in the context of a domestic robotic scenario. A dataset of sounds was recorded in realistic conditions (background noise, presence of several sound sources, reverberations, etc.) using the humanoid robot NAO. An extended benchmark is carried out to test a variety of representations combined with several classifiers. We provide results obtained with the annotated dataset and we assess the methods quantitatively on the basis of their classification scores, computation times and memory requirements. The annotated dataset is publicly available at https://team.inria.fr/perception/nard/.
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Maxime Janvier, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud. Sound Representation and Classification Benchmark for Domestic Robots. 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), May 2014, Hong-Kong, China. pp.6285-6292, ⟨10.1109/ICRA.2014.6907786⟩. ⟨hal-00952092⟩

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