A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Fundamentals of speech recognition
Fundamentals of speech recognition
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data
Signal Processing - Special section on information theoretic aspects of digital watermarking
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Machine Learning in Computer Vision (Computational Imaging and Vision)
Machine Learning in Computer Vision (Computational Imaging and Vision)
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Real-time detection of steam in video images
Pattern Recognition
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
New signal decomposition method based speech enhancement
Signal Processing
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 01
Handbook of Hidden Markov Models in Bioinformatics
Handbook of Hidden Markov Models in Bioinformatics
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut
Image and Vision Computing
Boundary refinements for wavelet-domain multiscale texture segmentation
Image and Vision Computing
An EM algorithm to learn sequences in the wavelet domain
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Computational methods for hidden Markov tree models-an application to wavelet trees
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
Pitch-synchronous wavelet representations of speech and musicsignals
IEEE Transactions on Signal Processing
Texture analysis with variational hidden Markov trees
IEEE Transactions on Signal Processing - Part I
Hidden Markov models for wavelet-based blind source separation
IEEE Transactions on Image Processing
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
Automatic recognition of ingestive sounds of cattle based on hidden Markov models
Computers and Electronics in Agriculture
Genetic wavelet packets for speech recognition
Expert Systems with Applications: An International Journal
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Hidden Markov models have been found very useful for a wide range of applications in machine learning and pattern recognition. The wavelet transform has emerged as a new tool for signal and image analysis. Learning models for wavelet coefficients have been mainly based on fixed-length sequences, but real applications often require to model variable-length, very long or real-time sequences. In this paper, we propose a new learning architecture for sequences analyzed on short-term basis, but not assuming stationarity within each frame. Long-term dependencies will be modeled with a hidden Markov model which, in each internal state, will deal with the local dynamics in the wavelet domain, using a hidden Markov tree. The training algorithms for all the parameters in the composite model are developed using the expectation-maximization framework. This novel learning architecture could be useful for a wide range of applications. We detail two experiments with artificial and real data: model-based denoising and speech recognition. Denoising results indicate that the proposed model and learning algorithm are more effective than previous approaches based on isolated hidden Markov trees. In the case of the 'Doppler' benchmark sequence, with 1024 samples and additive white noise, the new method reduced the mean squared error from 1.0 to 0.0842. The proposed methods for feature extraction, modeling and learning, increased the phoneme recognition rates in 28.13%, with better convergence than models based on Gaussian mixtures.