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Proceedings Paper

Sound-event classification using pseudo-color CENTRIST feature and classifier selection
Author(s): Jianfeng Ren; Xudong Jiang; Junsong Yuan
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Paper Abstract

Sound-event classification often extracts features from an image-like spectrogram. Recent approaches such as spectrogram image feature and subband-power-distribution image feature extract local statistics such as mean and variance from the spectrogram. We argue that such simple image statistics cannot well capture complex texture details of the spectrogram. Thus, we propose to extract pseudo-color CENTRIST features from the logarithm of Gammatone-like spectrogram. To well classify the sound event under the unknown noise condition, we propose a classifier-selection scheme, which automatically selects the most suitable classifier. The proposed approach is compared with the state of the art on the RWCP database, and demonstrates a superior performance.

Paper Details

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111C (11 July 2016); doi: 10.1117/12.2242357
Show Author Affiliations
Jianfeng Ren, Nanyang Technological Univ. (Singapore)
Xudong Jiang, Nanyang Technological Univ. (Singapore)
Junsong Yuan, Nanyang Technological Univ. (Singapore)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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