Concept# Filtre linéaire

Résumé

Un filtre linéaire est, en traitement du signal, un système qui applique un opérateur linéaire à un signal d'entrée. Les filtres linéaires sont rencontrés le plus souvent en électronique, mais il est possible d'en trouver en mécanique ou dans d'autres technologies.
Classification
Réponse impulsionnelle
Une réponse impulsionnelle est la sortie d'un système dont l'entrée est une impulsion de Dirac(\delta).
Les filtres linéaires peuvent être divisés en deux groupes : les filtres à réponse impulsionnelle infinie et les filtres à réponse impulsionnelle finie.
Pour ceux à réponse impulsionnelle finie, la sortie du système dépend uniquement de l'entrée alors que pour ceux à réponse impulsionnelle infinie, la sortie du système dépend à la fois de l'entrée et des sorties précédentes.
Réponse fréquentielle
Du point de vue fréquentiel, il existe plusieurs types courants de filtres linéaires :

- Les filtres passe-bas passent les basses fréquences e

Source officielle

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées

Chargement

Personnes associées

Chargement

Unités associées

Chargement

Concepts associés

Chargement

Cours associés

Chargement

Séances de cours associées

Chargement

Personnes associées (1)

Publications associées (35)

Chargement

Chargement

Chargement

Unités associées (2)

Concepts associés (23)

Filtre (audio)

Dans le traitement du signal, un filtre est un appareil ou une fonction servant à retirer ou bien à accentuer ou réduire certaines parties du spectre sonore représentées dans un signal.
Les filtres

Analogue filter

Analogue filters are a basic building block of signal processing much used in electronics. Amongst their many applications are the separation of an audio signal before application to bass, mid-range,

Filtre (électronique)

En électronique, un filtre est un circuit linéaire qui transmet une grandeur électrique (courant ou tension) selon sa répartition en fréquences. Le filtre transforme l'histoire de cette grandeur d'en

Cours associés (24)

The goal of this course is twofold: (1) to introduce physiological basis, signal acquisition solutions (sensors) and state-of-the-art signal processing techniques, and (2) to propose concrete examples of applications for vital sign monitoring and diagnosis purposes.

This course introduces the analysis and design of linear analog circuits based on operational amplifiers. A Laplace early approach is chosen to treat important concepts such as time and frequency responses, convolution, and filter design. The course is complemented with exercises and simulations.

Introduction to the basic techniques of image processing. Introduction to the development of image-processing software and to prototyping in JAVA. Application to real-world examples in industrial vision and biomedical imaging.

Binaural room impulse responses (BRIRs) characterize the transfer of sound from a source in a room to the left and right ear entrances of a listener. Applying BRIRs to sound source signals enables headphone listening with the perception of a three dimensional auditory image. BRIRs are usually linear filters of several hundred milliseconds to several seconds length. The waveforms of the BRIRs contain therefore a vast amount of information. This thesis studies the modeling of BRIRs with a reduced set of parameters. It is shown that late BRIR tails can be modeled perceptually accurately by considering only the time-frequency energy decay relief and frequency dependent interaural coherence (IC). This insight on BRIR modeling enables a number of algorithms with advantages over the previous state of the art. Three such algorithms are proposed: The first algorithm makes it possible to obtain BRIRs by measuring room properties and listener properties separately, vastly reducing the number of measurements necessary to measure listener-specific BRIRs for a number of listeners and rooms. The listener properties are measured as a head related transfer function (HRTF) set and the room properties are measured as a B-format1 room impulse response (RIR). It is shown how to combine the HRTF set of the listener with a B-format RIR to obtain BRIRs for that room individualized for the listener. This technique uses the insight on BRIR perception by computing the BRIR tail as a frequency dependent, linear combination of B-format channels, designed to obtain the desired energy decay relief and interaural coherence. A serious problem related to convolving sound source signals with BRIRs is the computational complexity of implementing long BRIRs as finite impulse response (FIR) filters. Inspired by the perceptual experiments on BRIR tails, a modified Jot reverberator is proposed, simulating BRIR tails with the desired frequency dependent interaural coherence, requiring significantly less computational power than direct application of BRIRs. Also inspired by the perception of BRIRs, an extension of this reverberator is proposed, modeling efficiently the reverberation tail with the correct coherence and also distinct early reflections using two parallel feedback delay networks. If stereo signals are played back using headphones, unnatural binaural cues are given to the listener, e.g. interaural level difference (ILD) changes not accompanied by corresponding interaural time difference (ITD) changes or diffuse sound with unnatural IC. In order to simulate stereo listening in a room and to avoid these unnatural cues, BRIRs can be applied to the left and right stereo channels. Besides the computational complexity associated with applying the BRIR filters, this technique has a number of disadvantages. The room associated with the used BRIRs is imposed on the stereo signal, which usually already contains reverberation and applying BRIRs leads to a change in reverberation time and to coloration. A technique is proposed in which the direct sound is rendered using data extracted from HRTFs and the ambient sound contained in the stereo signal is modified such that its coherence is matched to the coherence of a binaural recording of diffuse sound, without modifying its spectrum. Implementations of reverberators based on general feedback-delay networks (e.g. Jot reverberators) can require a high number of operations for implementing the so-called feedback matrix. For certain applications where the number of channels needs to be high, such as decorrelators, this can pose a real problem. Special types of matrices are known which can be implemented efficiently due to matrix elements having the same magnitude. However, the complexity can also be reduced by introducing many zero elements. Different types of such sparse feedback matrices are proposed and tested for their suitability in Jot reverberators. A highly efficient feedback matrix is obtained by combining both approaches, choosing the nonzero elements of a sparse matrix from efficiently implementable Hadamard matrices. ______________________________ 1 B-format refers to a 4-channel signal recorded with four coincident microphones: one omni and three dipole microphones pointing in orthogonal directions.

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well happen that the statistician only has access to a relatively low number of sparse measurements for each random curve. These discrete measurements may be moreover irregularly scattered in each curve's domain, missing altogether for some curves, and be contaminated by measurement noise. This sparse sampling protocol escapes from the reach of established estimators in functional time series analysis and therefore requires development of a novel methodology.
The core objective of this thesis is development of a non-parametric statistical toolbox for analysis of sparsely observed functional time series data. Assuming smoothness of the latent curves, we construct a local-polynomial-smoother based estimator of the spectral density operator producing a consistent estimator of the complete second order structure of the data. Moreover, the spectral domain recovery approach allows for prediction of latent curve data at a given time by borrowing strength from the estimated dynamic correlations in the entire time series across time. Further to predicting the latent curves from their noisy point samples, the method fills in gaps in the sequence (curves nowhere sampled), denoises the data, and serves as a basis for forecasting.
A classical non-parametric apparatus for encoding the dependence between a pair of or among a multiple functional time series, whether sparsely or fully observed, is the functional lagged regression model. This consists of a linear filter between the regressors time series and the response. We show how to tailor the smoother based estimators for the estimation of the cross-spectral density operators and the cross-covariance operators and, by means of spectral truncation and Tikhonov regularisation techniques, how to estimate the lagged regression filter and predict the response process.
The simulation studies revealed the following findings: (i) if one has freedom to design a sampling scheme with a fixed number of measurements, it is advantageous to sparsely distribute these measurements in a longer time horizon rather than concentrating over a shorter time horizon to achieve dense measurements in order to diminish the spectral density estimation error, (ii) the developed functional recovery predictor surpasses the static predictor not exploiting the temporal dependence, (iii) neither of the two considered regularisation techniques can, in general, dominate the other for the estimation in functional lagged regression models. The new methodologies are illustrated by applications to real data: the meteorological data revolving around the fair-weather atmospheric electricity measured in Tashkent, Uzbekistan, and at Wank mountain, Germany; and a case study analysing the dependence of the US Treasury yield curve on macroeconomic variables.
As a secondary contribution, we present a novel simulation method for general stationary functional time series defined through their spectral properties. A simulation study shows universality of such approach and superiority of the spectral domain simulation over the temporal domain in some situations.

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object positions and the appearance variations of object classes. This thesis improves upon several classical machine learning algorithms, enabling large computational gains in high dimensional feature space. A common trend in machine learning and computer vision research is to go large scale. In particular, the advent of huge datasets mined from the Internet, and the combination of multiple feature sources have considerably broadened the applications of computer vision. Tasks which were thought impossible a few years ago, such as human action recognition or pose estimation, automatic outdoor navigation, etc., now seem within reach. This dissertation is divided into two parts. The first one deals with the efficient training of a classifier or detector based on a large number of feature extractors, outside the control of the learning algorithm, and therefore of unknown suitability to the task at hand. More precisely, this part presents two kinds of strategies to accelerate the training of Boosting algorithms in such a context: (a) a method to better deal with the increasingly common case where features come from multiple sources (e.g. color, shape, texture, etc., in the case of images) and therefore can be partitioned into meaningful subsets; (b) new algorithms which balance at every Boosting iteration the number of weak learners and the number of training examples to look at in order to maximize the expected loss reduction. Experiments in image classification and object recognition on four standard computer vision datasets show that the adaptive techniques we propose outperform both basic sampling and state-of-the-art bandit methods. The second part deals with linear object detectors, currently the most popular class of detection systems, encompassing template matching, deformable part models, poselets, convolutional neural networks (which internally use linear filters), etc. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process and the linear filters. We make use of properties of the Fourier transform and clever implementation strategies to obtain a speedup factor proportional to the filter size, both while training and at test time. We also introduce a few modifications to the original Deformable Part Model (DPM) of Felzenszwalb et al. improving its detection accuracy. The gains in performance are demonstrated on the well-known Pascal VOC benchmark, where an increase by one order of magnitude in the speed of said convolutions, and an average improvement of 15% in the accuracy of the detector are established.

Séances de cours associées (57)