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Combining Wavelet-domain Hidden Markov Trees with Hidden Markov Models

Related publications (51)

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A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space

Samy Bengio, Jean-François Paiement

Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in music. In this paper, a distributed representation for chords is designed su ...

IDIAP Research Institute2005

Chord Representations for Probabilistic Models

Samy Bengio, Jean-François Paiement

Chord progressions are the building blocks from which tonal music is constructed. Inferring chord progressions is thus an essential step towards modeling long term dependencies in music. In this paper, three different representations for chords are designe ...

IDIAP2005

Extended Depth-of-Focus for Multi-Channel Microscopy Images: A Complex Wavelet Approach

Michaël Unser, Dimitri Nestor Alice Van De Ville, Daniel Sage

Microscopy imaging often suffers from limited depth-of-focus. However, the specimen can be “optically sectioned” by moving the object along the optical axis; different areas appear in focus in different images. Extended depth-of-focus is a fusion algorithm ...

IEEE2004

On Use of Task Independent Training Data in Tandem Feature Extraction

Hynek Hermansky

The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training dat ...

2004

Guest Editorial: Wavelets in Medical Imaging

Michaël Unser

I. Introduction Wavelets are the result of collective efforts that recognized common threads between ideas and concepts that had been independently developed and investigated by distinct research communities. They provide a unifying framework for decompos ...

2003

Speech recognition with auxiliary information

Automatic speech recognition (ASR) is a very challenging problem due to the wide variety of the data that it must be able to deal with. Being the standard tool for ASR, hidden Markov models (HMMs) have proven to work well for ASR when there are controls ov ...

EPFL2003

Speech Recognition with Auxiliary Information

IDIAP2003

Speech Recognition with Auxiliary Information

École Polytechnique Fédérale de Lausanne, Computer Science Department2003

Wavelets Versus Resels in the Context of fMRI: Establishing the Link with SPM

Michaël Unser, Dimitri Nestor Alice Van De Ville, Thierry Blu

Statistical Parametric Mapping (SPM) is a widely deployed tool for detecting and analyzing brain activity from fMRI data. One of SPM's main features is smoothing the data by a Gaussian filter to increase the SNR. The subsequent statistical inference is bas ...

SPIE2003

HMM mixtures (HMM2) for robust speech recognition

Katrin Weber

State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...

EPFL2003