Concept

Moving-average model

Related publications (99)

Average case analysis of multichannel sparse approximations using p-thresholding - art. no. 67011X

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This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2007

Average case analysis of multichannel thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

2007

Average case analysis of multichannel sparse approximations using p- thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces

p

-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latte ...

2007

On a stochastic version of the plenacoustic function

Martin Vetterli, Andrea Ridolfi, Luciano Sbaiz, Thibaut Ajdler

In this paper, we model a spatially varying channel where a source is moving along a random trajectory with respect to a fixed re- ceiver. The aim is to compute the power spectral density corre- sponding to the channel impulse response as a function of tem ...

2006

Speech Coding based on Spectral Dynamics

Petr Motlicek, Hynek Hermansky

In this paper we present first experimental results with a novel audio coding technique based on approximating Hilbert envelopes of relatively long segments of audio signal in critical-band-sized sub-bands by autoregressive model. We exploit the generalize ...

2006

Speech Coding based on Spectral Dynamics

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IDIAP2006

A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models

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Models dealing directly with the raw acoustic speech signal are an alternative to conventional feature-based HMMs. A popular way to model the raw speech signal is by means of an autoregressive (AR) process. Being too simple to cope with the nonlinearity of ...

IDIAP2006

Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces

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This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...

IDIAP2006

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In this paper, we describe additional experiments based on a novel audio coding technique that uses an autoregressive model to approximate an audio signal's Hilbert envelope. This technique is performed over long segments (1000 ms) in critical-band-sized s ...

IDIAP2006

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Average case analysis of multichannel sparse approximations using p- thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces

p

2007