13 pagesInternational audienceIt is a well known fact that the time-frequency domain is very well adapted for representing audio signals. The main two features of time-frequency representations of many classes of audio signals are sparsity (signals are generally well approximated using a small number of coefficients) and persistence (significant coefficients are not isolated, and tend to form clusters). This contribution presents signal approximation algorithms that exploit these properties, in the framework of hierarchical probabilistic models. Given a time-frequency frame (i.e. a Gabor frame, or a union of several Gabor frames or time-frequency bases), coefficients are first gathered into groups. A group of coefficients is then modeled as...
International audienceWe describe in this paper a fully Bayesian approach for sparse audio signal re...
International audienceSounds morphing is an important topic in signal processing of musical sounds a...
International audienceWe propose a new hybrid (or morphological) generative model that decomposes a ...
It is a well known fact that the time-frequency domain is very well adapted for representing audio s...
Sparse and structured signal expansions on dictionaries (i.e. a collection of elementary wave-forms,...
Many natural signals of practical interest are inherently sparse (or at least highly compressible) i...
Time-frequency representations are commonly used tools for the representation of audio and in partic...
International audienceThe readability of a time-frequency representation generally depend crucially ...
International audienceThis paper reports on recent results related to audiophonic signals encoding u...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceThe readability of a time-frequency representation generally depend crucially ...
International audienceWe describe in this paper a fully Bayesian approach for sparse audio signal re...
AbstractThis paper reports on recent results related to audiophonic signals encoding using time-scal...
International audienceWe describe in this paper a fully Bayesian approach for sparse audio signal re...
International audienceSounds morphing is an important topic in signal processing of musical sounds a...
International audienceWe propose a new hybrid (or morphological) generative model that decomposes a ...
It is a well known fact that the time-frequency domain is very well adapted for representing audio s...
Sparse and structured signal expansions on dictionaries (i.e. a collection of elementary wave-forms,...
Many natural signals of practical interest are inherently sparse (or at least highly compressible) i...
Time-frequency representations are commonly used tools for the representation of audio and in partic...
International audienceThe readability of a time-frequency representation generally depend crucially ...
International audienceThis paper reports on recent results related to audiophonic signals encoding u...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceTo display the time and frequency content of a given signal a large variety of...
International audienceThe readability of a time-frequency representation generally depend crucially ...
International audienceWe describe in this paper a fully Bayesian approach for sparse audio signal re...
AbstractThis paper reports on recent results related to audiophonic signals encoding using time-scal...
International audienceWe describe in this paper a fully Bayesian approach for sparse audio signal re...
International audienceSounds morphing is an important topic in signal processing of musical sounds a...
International audienceWe propose a new hybrid (or morphological) generative model that decomposes a ...