How sparsely can a signal be approximated while keeping its class identity?
| Autor: | Thomas Fillon, Manuel Moussallam, Laurent Daudet, Gael Richard |
|---|---|
| Přispěvatelé: | Deezer R&D, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Signal, Statistique et Apprentissage (S2A), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Institut Langevin - Ondes et Images (UMR7587) (IL), Sorbonne Université (SU)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Paris (UP)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2010 |
| Předmět: |
Class (set theory)
K-SVD Degree (graph theory) Computer science business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Sparse approximation Signal Identity (music) Power (physics) 030507 speech-language pathology & audiology 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0305 other medical science Representation (mathematics) business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ComputingMilieux_MISCELLANEOUS |
| Zdroj: | 3rd international workshop 3rd international workshop, Oct 2010, Firenze, France. pp.25, ⟨10.1145/1878003.1878012⟩ |
| DOI: | 10.1145/1878003.1878012 |
| Popis: | This paper explores the degree of sparsity of a signal approximation that can be reached while ensuring that a sufficient amount of information is retained, so that its main characteristics remains. Here, sparse approximations are obtained by decomposing the signals on an overcomplete dictionary of multiscale time-frequency "atoms". The resulting representation is highly dependent on the choice of dictionary, decomposition algorithm and depth of the decomposition. The class identity is measured by indirect means as the speech/music discrimination power of features derived from the sparse representation compared to those of classical PCM-based features. Evaluation is performed on French Broadcast TV and Radio recordings from the QUAERO project database with two different statistical classifiers. |
| Databáze: | OpenAIRE |
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