Consiglio Nazionale delle Ricerche

Tipo di prodottoCuratela di numero monografico (di rivista o di collana)
TitoloEditorial activity - Special Issue on Bayesian Source Separation
Anno di pubblicazione2007
Formato-
Autore/iKuruoglu E.; Knuth K.
Affiliazioni autoriCNR-ISTI, Pisa, Italy; University at Albany, NY, USA
Autori CNR e affiliazioni
  • ERCAN ENGIN KURUOGLU
Lingua/e
  • inglese
SintesiThe signal processing problem known as source separation has rapidly grown in the last decade from being viewed as something of a curiosity to becoming a fundamental signal processing problem that is ubiquitous across scientific disciplines. Source separation problems are characterized by a set of sources that either emit or modulate signals that propagate to one or several detectors. The signal processing goal is to "separate" the recorded signals into the set of "source" signals. This basic situation appears in a wide variety of contexts ranging from the more traditional mixing of sound signals as in the Cocktail Party Problem to mixtures of source signals of spatial extent in images, such as in astrophysical applications where the objects of study are optically thin (transparent) or magnetic resonance imaging where the effects of several distinct processes are superimposed. At this point in time, source separation has been widely studied, resulting in an extensive array of source separation algorithms. Much of the effort has gone into developing what are known as blind source separation algorithms, referring to the fact that these algorithms are provided with a minimum amount of information about the nature of the recorded signals. These blind algorithms are extremely useful, as they are specifically designed to be generally applicable to a wide array of problems. Many of these techniques work well in a wide variety of situations, including those where noise is an issue.
Lingua sintesieng
Altra sintesi-
Lingua altra sintesi-
Pagine da855
Pagine a857
Pagine totali-
Serie/CollanaDigital signal processing (Print)
Attiva dal 1991
Editore: Academic Press, - Duluth, MN
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1051-2004
Titolo chiave: Digital signal processing (Print)
Titolo proprio: Digital signal processing. (Print)
Titolo abbreviato: Digit. signal process. (Print)
Numero volume della serie/collana17
ISBN-
DOI-
Editore-
Edizione/Versione-
Verificato da referee-
Stato della pubblicazionePublished version
Parole chiaveBayesian source separation, Independent component analysis
Link (URL, URI)-
Titolo parallelo-
Note/Altre informazioniA cura di: Ercan Engin Kuruoglu, Kevin Knuth. Fascicolo: 5.
Strutture CNR
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P10.012.001 : Elaborazione di segnali e immagini per impieghi diagnostici e interpretazione di immagini multisorgente
Progetti Europei-
Allegati
Editorial activity - Special Issue on Bayesian Source Separation (documento privato )
Descrizione: Codice PuMa: cnr.isti/2007-ED-003
Tipo documento: application/pdf

Dati associati a vecchie tipologie
I dati associati a vecchie tipologie non sono modificabili, derivano dal cambiamento della tipologia di prodotto e hanno solo valore storico.
RivistaDigital signal processing (Print)
Attiva dal 1991
Editore: Academic Press, - Duluth, MN
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 1051-2004
Titolo chiave: Digital signal processing (Print)
Titolo proprio: Digital signal processing. (Print)
Titolo abbreviato: Digit. signal process. (Print)
AbstractThe signal processing problem known as source separation has rapidly grown in the last decade from being viewed as something of a curiosity to becoming a fundamental signal processing problem that is ubiquitous across scientific disciplines. Source separation problems are characterized by a set of sources that either emit or modulate signals that propagate to one or several detectors. The signal processing goal is to "separate" the recorded signals into the set of "source" signals. This basic situation appears in a wide variety of contexts ranging from the more traditional mixing of sound signals as in the Cocktail Party Problem to mixtures of source signals of spatial extent in images, such as in astrophysical applications where the objects of study are optically thin (transparent) or magnetic resonance imaging where the effects of several distinct processes are superimposed. At this point in time, source separation has been widely studied, resulting in an extensive array of source separation algorithms. Much of the effort has gone into developing what are known as blind source separation algorithms, referring to the fact that these algorithms are provided with a minimum amount of information about the nature of the recorded signals. These blind algorithms are extremely useful, as they are specifically designed to be generally applicable to a wide array of problems. Many of these techniques work well in a wide variety of situations, including those where noise is an issue.
N. volume della rivista17

Dati storici
I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
Area disciplinareElectrical & Electronics Engineering
Area valutazione CIVRScienze matematiche e informatiche
Rivista ISIDIGITAL SIGNAL PROCESSING [11305J0]
NoteIn: Digital Signal Processing. Editorial, vol. 17 (5) pp. 855 - 857. Ercan Engin Kuruoglu, Kevin Knuth (eds.). Elsevier, 2007.