Sound-Event Recognition with a Companion Humanoid

Maxime Janvier 1 Xavier Alameda-Pineda 1, * Laurent Girin 1, 2 Radu Horaud 1
* Corresponding author
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 : In this paper we address the problem of recognizing everyday sound events in indoor environments with a consumer robot. Sounds are represented in the spectro-temporal domain using the stabilized auditory image (SAI) representation. The SAI is well suited for representing pulse-resonance sounds and has the interesting property of mapping a time-varying signal into a fixed-dimension feature vector space. This allows us to map the sound recognition problem into a supervised classification problem and to adopt a variety of classifications schemes. We present a complete system that takes as input a continuous signal, splits it into significant isolated sounds and noise, and classifies the isolated sounds using a catalogue of learned sound-event classes. The method is validated with a large set of audio data recorded with a humanoid robot in a house. Extended experiments show that the proposed method achieves state-of-the-art recognition scores with a twelve-class problem, while requiring extremely limited memory space and moderate computing power. A first real-time embedded implementation in a consumer robot show its ability to work in real conditions.
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https://hal.inria.fr/hal-00768767
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Maxime Janvier, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud. Sound-Event Recognition with a Companion Humanoid. Humanoids 2012 - IEEE International Conference on Humanoid Robotics, Nov 2012, Osaka, Japan. pp.104-111, ⟨10.1109/HUMANOIDS.2012.6651506⟩. ⟨hal-00768767⟩

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