In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.
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Yu Gwang JIN, Nam Soo KIM, "On Detecting Target Acoustic Signals Based on Non-negative Matrix Factorization" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 4, pp. 922-925, April 2010, doi: 10.1587/transinf.E93.D.922.
Abstract: In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.922/_p
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@ARTICLE{e93-d_4_922,
author={Yu Gwang JIN, Nam Soo KIM, },
journal={IEICE TRANSACTIONS on Information},
title={On Detecting Target Acoustic Signals Based on Non-negative Matrix Factorization},
year={2010},
volume={E93-D},
number={4},
pages={922-925},
abstract={In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.},
keywords={},
doi={10.1587/transinf.E93.D.922},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - On Detecting Target Acoustic Signals Based on Non-negative Matrix Factorization
T2 - IEICE TRANSACTIONS on Information
SP - 922
EP - 925
AU - Yu Gwang JIN
AU - Nam Soo KIM
PY - 2010
DO - 10.1587/transinf.E93.D.922
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2010
AB - In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.
ER -