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Publications associées (112)

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

We cast the query by example spoken term detection (QbE-STD) problem as subspace detection where query and background subspaces are modeled as union of low-dimensional subspaces. The speech exemplars used for subspace modeling are class-conditional posteri ...

Idiap2016

Image Based Relighting of Cultural Artifacts

Niranjan Thanikachalam

By incorporating computational methods into the image acquisition pipeline, computational photography has opened up new avenues in the representation and visualization of real world objects in the digital world. For example, we can sample a scene under a f ...

EPFL2016

Sparse Subspace Modeling for Query by Example Spoken Term Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

We cast the problem of query by example spoken term detection (QbE-STD) as subspace detection where query and background are modeled as a union of low-dimensional subspaces. The speech exemplars used for subspace modeling consist of class-conditional poste ...

Idiap2016

Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of lowdimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representa ...

ICASSP2016

Exploiting Low-dimensional Structures to Enhance DNN based Acoustic Modeling in Speech Recognition

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low- dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse represen ...

IEEE2016

Seismic Simultaneous Source Separation via Patchwise Sparse Representation

Pascal Frossard, Yiren Zhou

The concept of simultaneous source has recently become of interest in seismic exploration, due to its efficient or economic acquisition or both. The blended data overlapped between shot records are acquired in simultaneous source acquisition. Separating th ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Dictionary Learning Over Distributed Models

Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over ...

IEEE2015

Multi-Graph Learning of Spectral Graph Dictionaries

Pascal Frossard

We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that pro ...

2015

Crowdsourcing Microdata for Cost-Effective and Reliable Lexicography

Martin Benjamin

Lexicography has long faced the challenge of having too few specialists to document too many words in too many languages with too many linguistic features. Great dictionaries are invariably the product of many person-years of labor, whether the lifetime wo ...

2015

Dictionary learning for fast classification based on soft-thresholding

Pascal Frossard, Alhussein Fawzi

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

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