Presentation 2010-09-05
Sound Source Detection by Learning
Chihiro IKEDA, Yaokai FENG, Seiichi UCHIDA,
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Abstract(in English) Sound source detection in an image is a difficult inverse problem where the pixels belonging to the sound source area are to be estimated. The purpose of this paper is to consider an accurate sound source detection method by using machine learning framework. Specifically, the proposed method relies on an AdaBoost-based learning scheme for discriminating whether each pixel belongs to a sound source or not. The learning is done by training weak learners to discriminate positive samples (couples of image features around sound sources and audio features) and negative samples (couples of image features distant from sound sources and audio features). This learning scheme simply combines these multimodal information (i.e., image and audio) by using some weak learners to discriminate the samples by a single image feature and others by a single audio feature. The performance of this naive implementation based on a simple combination of multimodal information was examined experimentally and its essential problem was revealed with a possible remedy.
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Keyword(in English) sound source detection / learning / AdaBoost
Paper # PRMU2010-69,IBISML2010-41
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Conference Information
Committee PRMU
Conference Date 2010/8/29(1days)
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Paper Information
Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sound Source Detection by Learning
Sub Title (in English)
Keyword(1) sound source detection
Keyword(2) learning
Keyword(3) AdaBoost
1st Author's Name Chihiro IKEDA
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Yaokai FENG
2nd Author's Affiliation Faculity of Information Science and Electrical Engineering, Kyushu University
3rd Author's Name Seiichi UCHIDA
3rd Author's Affiliation Faculity of Information Science and Electrical Engineering, Kyushu University
Date 2010-09-05
Paper # PRMU2010-69,IBISML2010-41
Volume (vol) vol.110
Number (no) 187
Page pp.pp.-
#Pages 6
Date of Issue