Fundamentals of speech recognition
Fundamentals of speech recognition
Machine Learning
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Building sparse support vector machines for multi-instance classification
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
A Survey of Audio-Based Music Classification and Annotation
IEEE Transactions on Multimedia
Audio classification based on MPEG-7 spectral basis representations
IEEE Transactions on Circuits and Systems for Video Technology
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Cepstral features have been widely used in audio applications. Domain knowledge has played an important role in designing different types of cepstral features proposed in the literature. In this paper, we present a novel approach for learning optimized cepstral features directly from audio data to better discriminate between different categories of signals in classification tasks. We employ multi-layer feed-forward neural networks to model the cepstral feature extraction process. The network weights are initialized to replicate a reference cepstral feature like the mel frequency cepstral coefficient. We then propose a embedded approach that integrates feature learning with the training of a support vector machine (SVM) classifier. A single optimization problem is formulated where the feature and classifier variables are optimized simultaneously so as to refine the initial features and minimize the classification risk. Experimental results have demonstrated the effectiveness of the proposed feature learning approach, outperforming competing methods by a large margin on benchmark data.