Structured Sparse Coding for Microphone Array Location Calibration
Graph Chatbot
Chattez avec Graph Search
Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
With the flood of information available today the question how to deal with high dimensional data/signals, which are cumbersome to handle, to calculate with and to store, is highly important. One approach to reducing this flood is to find sparse signal rep ...
This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabilistic signal model which assumes randomness for both the amplitude and also ...
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 recovery of speech. We formulate the underdetermined convolutive speech separati ...
We introduce a new signal model, called (K,C)-sparse, to capture K-sparse signals in N dimensions whose nonzero coefficients are contained within at most C clusters, with C < K < N. In contrast to the existing work in the sparse approximation and compress ...
The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...
Typical tasks in signal processing may be done in simpler ways or more efficiently if the signals to analyze are represented in a proper way. This thesis deals with some algorithmic problems related to signal approximation, more precisely, in the novel fie ...
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 ...
Acoustic echo control and noise suppression is an important part of any "handsfree" telecommunication system, such as telephony or audio or video conferencing systems. Bandwidth and computational complexity constraints have prevented that stereo or multi-c ...
Despite the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation are limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertic ...
The theory of Compressed Sensing (CS) is based on reconstructing sparse signals from random linear measurements. As measurement of continuous signals by digital devices always involves some form of quantization, in practice devices based on CS encoding mus ...