TY - GEN
T1 - A novel framework based on trace norm minimization for audio event detection
AU - Shi, Ziqiang
AU - Han, Jiqing
AU - Zheng, Tieran
PY - 2011
Y1 - 2011
N2 - In this paper, a novel framework based on trace norm minimization for audio event detection is proposed. In the framework, both the feature extraction and pattern classifier are made by solving corresponding convex optimization problem with trace norm regularization or under trace norm constraint. For feature extraction, robust principle component analysis (robust PCA) via minimizing a combination of the nuclear norm and the ℓ1-norm is used to extract matrix representation features which is robust to outliers and gross corruption for audio segments. These matrix representation features are fed to a linear classifier where the weight matrix and bias are learned by solving similar trace norm regularized problems. Experiments on real data sets indicate that this novel framework is effective and noise robust.
AB - In this paper, a novel framework based on trace norm minimization for audio event detection is proposed. In the framework, both the feature extraction and pattern classifier are made by solving corresponding convex optimization problem with trace norm regularization or under trace norm constraint. For feature extraction, robust principle component analysis (robust PCA) via minimizing a combination of the nuclear norm and the ℓ1-norm is used to extract matrix representation features which is robust to outliers and gross corruption for audio segments. These matrix representation features are fed to a linear classifier where the weight matrix and bias are learned by solving similar trace norm regularized problems. Experiments on real data sets indicate that this novel framework is effective and noise robust.
KW - Audio event detection
KW - Low-rank matrix
KW - Matrix classification
KW - Robust principle component analysis
KW - Trace norm minimization
UR - https://www.scopus.com/pages/publications/81855227214
U2 - 10.1007/978-3-642-24958-7_75
DO - 10.1007/978-3-642-24958-7_75
M3 - 会议稿件
AN - SCOPUS:81855227214
SN - 9783642249570
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 646
EP - 654
BT - Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
T2 - 18th International Conference on Neural Information Processing, ICONIP 2011
Y2 - 13 November 2011 through 17 November 2011
ER -