Publications by Volkan Cevher | EPFL Graph Search

Room Acoustic Modeling Exploiting Joint Sparsity and Low-rank Structures

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammad Golbabaee

A novel formulation of acoustic multipath is proposed for estimation of the room acoustic using recordings of unknown concurrent speech sources at unknown locations. The framework exploits sparsity and low-rank structures characterized by the Image method ...

2013

A Multipath Sparse Beamfroming Method

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

A novel formulation of beamforming is proposed for acquisition of the signals in reverberant acoustic clutter of interferences and noise. We derive the beamforming methods which incorporate the sparsity structure pertained to the acoustic source distributi ...

2013

Convexity in source separation: Models, geometry, and algorithms

Volkan Cevher, Afsaneh Asaei, Quoc Tran Dinh, Luca Baldassarre

Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, bl ...

2013

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Volkan Cevher, Anastasios Kyrillidis

The convex l(1)-regularized log det divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the l(1)-regularization is nonsmooth, the log det objective is not globally Li ...

IEEE2013

Structured Sparse Acoustic Modeling for Speech Separation

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei, Mohammad Golbabaee

A novel formulation of acoustic multipath is proposed for estimation of the room acoustic using recordings of unknown concurrent speech sources at unknown locations. The framework exploits sparsity and low-rank structures characterized by the Image method ...

SPARS2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

IEEE2013

Equivalence of synthesis and atomic formulations of sparse recovery

Volkan Cevher, Mitra Fatemi, Shayan Dashmiz

Recovery of sparse signals from linear, dimensionality reducing measurements broadly fall under two well-known formulations, named the synthesis and the analysis a ́ la Elad et al. Recently, Chandrasekaran et al. introduced a new algorithmic sparse recover ...

Ieee2012

Active Learning of Multi-Index Function Models

Volkan Cevher, Hemant Tyagi

We consider the problem of actively learning \textit{multi-index} functions of the form

f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx)

from point evaluations of

f

. We assume that the function

f

is defined on an

\ell_2

-ball in

\Real^d

,

g

is twice contin ...

2012

Hard Thresholding with Norm Constraints

Volkan Cevher, Gilles Puy, Anastasios Kyrillidis

We introduce a new sparse recovery paradigm, called Normed Pursuits, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that Normed Pur ...

Ieee2012

Learning Ridge Functions With Randomized Sampling In High Dimensions

Volkan Cevher, Hemant Tyagi

We study the problem of learning ridge functions of the form f(x) = g(aT x), x ∈ ℝd, from random samples. Assuming g to be a twice continuously differentiable function, we leverage techniques from low rank matrix recovery literature to derive a uniform app ...

IEEE2012

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Room Acoustic Modeling Exploiting Joint Sparsity and Low-rank Structures

A Multipath Sparse Beamfroming Method

Convexity in source separation: Models, geometry, and algorithms

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Structured Sparse Acoustic Modeling for Speech Separation

Manifold Sparse Beamforming

Equivalence of synthesis and atomic formulations of sparse recovery

Active Learning of Multi-Index Function Models

Hard Thresholding with Norm Constraints

Learning Ridge Functions With Randomized Sampling In High Dimensions

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection

Manifold Sparse Beamforming

Room Acoustic Modeling Exploiting Joint Sparsity and Low-rank Structures

Convexity in source separation: Models, geometry, and algorithms

Active Learning of Multi-Index Function Models

Equivalence of synthesis and atomic formulations of sparse recovery

Structured Sparse Acoustic Modeling for Speech Separation

A Multipath Sparse Beamfroming Method

Hard Thresholding with Norm Constraints

Learning Ridge Functions With Randomized Sampling In High Dimensions