Daily sound recognition using pitch-cluster-maps for mobile robot audition

Authors:
Yoko Sasaki;Masahito Kaneyoshi;Satoshi Kagami;Hiroshi Mizoguchi;Tadashi Enomoto
Affiliations:
Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Faculty of Mechanical Engineering, Department of Science and Technology, Tokyo University of Science, Noda-shi, Chiba, Japan and Digital Human Research Center, National Institute of Advanced Indus ...;Kansai Electric Power Co. Inc., Amagasaki, Hyogo, Japan
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 4
Cited 1

Comparative Study of Speaker Identification Methods: dPLRM, SVM and GMM

IEICE - Transactions on Information and Systems
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications

Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Bathroom activity monitoring based on sound

PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Model-based sequential organization in cochannel speech

IEEE Transactions on Audio, Speech, and Language Processing

Cluster self-organization of known and unknown environmental sounds using recurrent neural network

ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a sound identification method for a mobile robot in home and office environment. We propose a simple sound database called Pitch-Cluster-Maps(PCMs) based on Vector Quantization approach. Binarized frequency spectrum is used for PCMs codebook generation. It can describe a variety of sound sources, not only voice, from short term sound input. The proposed PCMs sound identification requires several tens(msec) of sound input, and is suitable for a mobile robot application which condition is dynamically changing. We implemented the proposed method on our mobile robot audition system equipped with a 32ch microphone array. Robot noise reduction using proposed PCMs recognition is applied to each input signal of a microphone array. The performance of daily sound recognition for separated sound sources from robot in motion is evaluated.