Neural Processing Letters: Volume 42 Issue 2, October 2015
Publisher: Kluwer Academic Publishers
We propose an algorithm to do audio signal classification based on low-rank matrix representative audio data. Conventionally, the low-rank matrix data can be represented by a vector in high dimensional space. Some learning algorithms are then applied in such a vector space for matrix data classification. Particularly, maximum margin classifiers, ...
Audio classification, Maximum margin, Trace norm regularization, Low-rank feature
Digital Signal Processing: Volume 32, September, 2014
Publisher: Academic Press, Inc.
Channel distortion is one of the major factors which degrade the performances of automatic speech recognition (ASR) systems. Current compensation methods are generally based on the assumption that the channel distortion is a constant or slowly varying bias in an utterance or globally. However, this assumption is not sustained in ...
Expectation-maximization, Channel distortion, Spectrum missing, Automatic speech recognition
Circuits, Systems, and Signal Processing: Volume 33 Issue 7, July 2014
Publisher: Birkhauser Boston Inc.
Traditionally, most of voice activity detection (VAD) methods are based on speech features such as spectrum, temporal energy, and periodicity. The robustness of these features plays a critical role on the performance of VAD. However, since these features are always directly generated from observed signal, the robustness of these features ...
Optimization algorithm, Learned dictionary, Mutual coherence, Sparse representation, Voice activity detection (VAD)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining: Volume 5 Issue 1, December 2013
Citation Count: 3
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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 ...
robust principle component analysis, Audio classification, online learning, proximal gradient, matrix classification, trace norm minimization, low-rank matrix
IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Publisher: AAAI Press
This paper studies the recovery guarantees of the models of minimizing ||χ|| * + 1/2α||χ|| F 2 where χ is a tensor and ||χ|| * and ||χ|| F are the trace and Frobenius norm of respectively. We show that they can efficiently recover low-rank tensors. In particular, they enjoy exact ...
IEEE Transactions on Audio, Speech, and Language Processing: Volume 21 Issue 3, March 2013
Publisher: IEEE Press
In this paper, we generalize the Gaussian Mixture Model (GMM) in two ways: a) by introducing novel distance measures between two vectors based on nonlinear maps to give more general mixture models; b) by building mixture models based on multiple different kinds of distributions. These two generalizations cope with different ...
IEEE Transactions on Audio, Speech, and Language Processing: Volume 21 Issue 1, January 2013
Publisher: IEEE Press
In order to naturally combine audio information from different dimensions and build robust audio processing system, a novel framework based on low-rank tensor representation features for audio segment classification is proposed in this paper. The audio signal is first transformed into tensor format data, and then these tensor data are ...
ICONIP'11: Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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 ...
low-rank matrix, audio event detection, robust principle component analysis, matrix classification, trace norm minimization