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| dc.contributor | Universitat de Vic. Escola Politècnica Superior |
|---|---|
| dc.contributor | Universitat de Vic. Grup de Recerca en Tecnologies Digitals |
| dc.contributor | International Conference on Spoken Language Processing ( 6ena : 2000 : Pekín) |
| dc.contributor | ICSLP2000 |
| dc.contributor.author | Solé-Casals, Jordi |
| dc.contributor.author | Jutten, Christian |
| dc.contributor.author | Taleb, Anisse |
| dc.date | 2000 |
| dc.identifier | http://hdl.handle.net/10854/2782 |
| dc.identifier.uri | http://hdl.handle.net/10854/2782 |
| dc.description | The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach. |
| dc.format | application/pdf |
| dc.language | eng |
| dc.rights | Aquest document està subjecte a aquesta llicència Creative Commons |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.subject | Processament de la parla |
| dc.title | Source separation techniques applied to linear prediction |
| dc.type | info:eu-repo/semantics/conferenceObject |
Text complet d'aquest document
| Fitxers | Mida | Format | Visualitza |
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| artconlli_a2000 ... ordi_source_separation.pdf | 103.5 KB | application/pdf | Visualitza/Obre |