Publications related to Reverberant Audio Source Separation via Sparse and Low-Rank Modeling

Compressed sensing with signal averaging for improved sensitivity and motion artifact reduction in fluorine-19 MRI

Jérôme Yerly, Ruud van Heeswijk, Elena Najdenovska, Tobias Kober, Tom Hilbert

Fluorine-19 (F-19) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non-invasive quantification of inflammation and cell tracking, but suffers from a low signal-to-noise ratio and extended scan time. To address this limitation, we tested the ...

WILEY2020

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Luc Thévenaz, Simon Adrien Zaslawski, Zhisheng Yang

The benefits and limitations inherent to the 2D post-processing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estim ...

IEEE Institute of Electrical and Electronics Engineers2020

Ultra Low Power FM-UWB Transceiver for High-Density Wireless Sensor Networks

Vladimir Kopta

The WiseSkin project aims to provide a non-invasive solution for restoration of a natural sense of touch to persons using prosthetic limbs. By embedding sensor nodes into the silicone coating of the prosthesis, which acts as a sensory skin, WiseSkin target ...

EPFL2018

Objective evaluation of static beamforming on the quality of speech in noise

Hervé Lissek, Gilles André Courtois, Vincent Pierre Olivier Grimaldi

Beamforming is commonly used in devices such as in hearing instruments (HI) with a view to reducing noise. It consists in focusing on audio signals arising from a prescribed steering direction while suppressing those coming from other directions. The effic ...

European Acoustics Association2018

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Dictionary Learning Based on Sparse Distribution Tomography

Michaël Unser, Pedram Pad

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

P. Pad2017

High sensitivity field asymmetric ion mobility spectrometer

Philippe Marmillod, Giovanni Boero, Jürgen Brugger, Mario Andres Chavarria Varon, Alessandro Valentino Matheoud

A high sensitivity field asymmetric ion mobility spectrometer (FAIMS) was designed, fabricated, and tested. The main components of the system are a 10.6 eV UV photoionization source, an ion filter driven by a high voltage/high frequency n-MOS inverter circ ...

American Institute of Physics2017

Enhancing Measurement Quality through Active Sampling in Mobile Air Quality Monitoring Sensor Networks

Alcherio Martinoli, Adrian Arfire, Ali Marjovi

In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these systems make use of some sort of mobility t ...

IEEE2016

Multilinear Low-Rank Tensors on Graphs & Applications

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank S ...

2016

Simultaneous temporal superresolution and denoising for cardiac fluorescence microscopy

Michael Stefan Daniel Liebling

Due to low light emission of fluorescent samples, live fluorescence microscopy imposes a tradeoff between spatiotemporal resolution and signal-to-noise ratio. This can result in images and videos containing motion blur or Poisson-type shot noise, depending ...

2016

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

CPT Cesium-Cell Atomic Clock Operation With a 12-mW Frequency Synthesizer ASIC

Steve Tanner, Pierre-André Farine, Yazhou Zhao

In this paper, we present the design, fabrication, and electrical characterization of a low-power microwave source for interrogation of cesium atomic hyperfine transition frequency using the coherent population trapping (CPT) technique. The 4.6-GHz frequen ...

Institute of Electrical and Electronics Engineers2015

Spinor stochastic resonance

Benoît Marie Joseph Deveaud, François Morier-Genoud, Marcia Portella Oberli, Hadis Abbaspour, Stéphane Trebaol

We report on noise-induced spin ordering in a collective quasiparticle system: spinor stochastic resonance. Synergetic interplay of a polarization-modulated signal and a polarization noise allows us to switch coherently between the two metastable states of ...

American Physical Society2015

Metric Learning with Rank and Sparsity Constraints

Volkan Cevher, Baran Gözcü, Bubacarr Bah

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

Ieee2014

Stochastic Resonance in Collective Exciton-Polariton Excitations inside a GaAs Microcavity

Benoît Marie Joseph Deveaud, François Morier-Genoud, Marcia Portella Oberli, Hadis Abbaspour, Stéphane Trebaol

We report the first observation of stochastic resonance in confined exciton polaritons. We evidence this phenomena by tracking the polaritons behavior through two stochastic resonance quantifiers namely the spectral magnification factor and the signal-to-n ...

American Physical Society2014

Variational Justification of Cycle Spinning for Wavelet-Based Solutions of Inverse Problems

Michaël Unser, Ulugbek Kamilov, Emrah Bostan

Cycle spinning is a widely used approach for improving the performance of wavelet-based methods that solve linear inverse problems. Extensive numerical experiments have shown that it significantly improves the quality of the recovered signal without increa ...

IEEE Institute of Electrical and Electronics Engineers2014

Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection

Giovanni De Micheli, Majid Yazdani, Hassan Ghasemzadeh Mohammadi, Pierre-Emmanuel Julien Marc Gaillardon

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling cha ...

2014

Sparse Reverberant Audio Source Separation via Reweighted Analysis

Jean-Philippe Thiran, Pierre Vandergheynst, Yves Wiaux, Rafael Eduardo Carrillo Rangel

We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse p ...

Institute of Electrical and Electronics Engineers2013

Compressed Optical Imaging

Aurélien Bourquard

We address the resolution of inverse problems where visual data must be recovered from incomplete information optically acquired in the spatial domain. The optical acquisition models that are involved share a common mathematical structure consisting of a l ...

EPFL2013

Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Michael Christoph Gastpar

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear mea- surements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the m ...

Institute of Electrical and Electronics Engineers2013