Publications de Volkan Cevher | EPFL Graph Search

A Game Theoretic Approach to Expander-based Compressive Sensing

We consider the following expander-based compressive sensing (e-CS) problem: Given Φ ∈ ℝM×N (M

IEEE2011

Compressive sensing meets game theory

We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in under-determined linear regression problems. Given ƒ = Φα* + μ, the MUSE provably and efficiently finds a k-sparse vector α̂ such that ∥Φα̂ − ƒ∥∞ ≤ ∥ ...

IEEE2011

Learning Low-Dimensional Signal Models

Volkan Cevher

Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weather-monitoring applications, produce a data deluge that severely stresses the avail able analog-to-digital converter, communication bandwidth, and digital-storag ...

Institute of Electrical and Electronics Engineers2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

Idiap2011

An Alps View of Sparse Recovery

Volkan Cevher

We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra o ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

IEEE Service Center, 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331 USA2011

Recipes on Hard Thresholding Methods

Volkan Cevher, Anastasios Kyrillidis

Compressive sensing (CS) is a data acquisition and recovery technique for finding sparse solutions to linear inverse problems from sub-Nyquist measurements. CS features a wide range of computationally efficient and robust signal recovery methods, based on ...

2011

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

2011

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammadjavad Taghizadeh

We study the sparsity of spectro-temporal representation of speech in reverberant acoustic conditions. This study motivates the use of structured sparsity models for efﬁcient speech recovery. We formulate the underdetermined convolutive speech separation i ...

2011

Compressive Sensing under Matrix Uncertainties: An Approximate Message Passing Approach

Volkan Cevher

In this work, we consider a general form of noisy compressive sensing (CS) when there is uncertainty in the measurement matrix as well as in the measurements. Matrix uncertainty is motivated by practical cases in which there are imperfections or unknown ca ...

2011

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A Game Theoretic Approach to Expander-based Compressive Sensing

Compressive sensing meets game theory

Learning Low-Dimensional Signal Models

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

An Alps View of Sparse Recovery

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Recipes on Hard Thresholding Methods

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Compressive Sensing under Matrix Uncertainties: An Approximate Message Passing Approach

Learning Low-Dimensional Signal Models

A Game Theoretic Approach to Expander-based Compressive Sensing

Recipes on Hard Thresholding Methods

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Compressive sensing meets game theory

An Alps View of Sparse Recovery

Multi-party Speech Recovery Exploiting Structured Sparsity Models

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Compressive Sensing under Matrix Uncertainties: An Approximate Message Passing Approach