Convex Optimization
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Highlight sound effects detection in audio stream
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Classifying matrices with a spectral regularization
Proceedings of the 24th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A novel framework based on trace norm minimization for audio event detection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Foundations and Trends® in Machine Learning
Audio Signal Feature Extraction and Classification Using Local Discriminant Bases
IEEE Transactions on Audio, Speech, and Language Processing
Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Combination
IEEE Transactions on Multimedia
Content-Based Information Fusion for Semi-Supervised Music Genre Classification
IEEE Transactions on Multimedia
A Survey of Audio-Based Music Classification and Annotation
IEEE Transactions on Multimedia
An implementable proximal point algorithmic framework for nuclear norm minimization
Mathematical Programming: Series A and B
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In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination of the nuclear norm and the ℓ1-norm is used to extract low-rank matrix features which are robust to white noise and gross corruption for audio signal. These low-rank matrix features are fed to a linear classifier where the weight and bias are learned by solving similar trace norm constrained problems. For this linear classifier, most methods find the parameters, that is the weight matrix and bias in batch-mode, which makes it inefficient for large scale problems. In this article, we propose a parallel online framework using accelerated proximal gradient method. This framework has advantages in processing speed and memory cost. In addition, as a result of the regularization formulation of matrix classification, the Lipschitz constant was given explicitly, and hence the step size estimation of the general proximal gradient method was omitted, and this part of computing burden is saved in our approach. Extensive experiments on real data sets for laugh/non-laugh and applause/non-applause classification indicate that this novel framework is effective and noise robust.