Performance Analysis of Source Image Estimators in Blind Source Separation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004540" target="_blank" >RIV/46747885:24220/17:00004540 - isvavai.cz</a>

Výsledek na webu

<a href="http://xplorestaging.ieee.org/ielx7/78/7942328/07934340.pdf?arnumber=7934340" target="_blank" >http://xplorestaging.ieee.org/ielx7/78/7942328/07934340.pdf?arnumber=7934340</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/tsp.2017.2709269" target="_blank" >10.1109/tsp.2017.2709269</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Performance Analysis of Source Image Estimators in Blind Source Separation

Popis výsledku v původním jazyce

Blind methods often separate or identify signals or signal subspaces up to an unknown scaling factor. Sometimes it is necessary to cope with the scaling ambiguity, which can be done through reconstructing signals as they are received by sensors, because scales of the sensor responses (images) have known physical interpretations. In this paper, we analyze two approaches that are widely used for computing the sensor responses, especially, in Frequency-Domain Independent Component Analysis. One approach is the least-squares projection, while the other one assumes a regular mixing matrix and computes its inverse. Both estimators are invariant to the unknown scaling. Although frequently used, their differences were not studied yet. A goal of this work is to fill this gap. The estimators are compared through a theoretical study, perturbation analysis and simulations. We point to the fact that the estimators are equivalent when the separated signal subspaces are orthogonal, and vice versa. Two applications are shown, one of which demonstrates a case where the estimators yield substantially different results.

Název v anglickém jazyce

Performance Analysis of Source Image Estimators in Blind Source Separation

Popis výsledku anglicky

Blind methods often separate or identify signals or signal subspaces up to an unknown scaling factor. Sometimes it is necessary to cope with the scaling ambiguity, which can be done through reconstructing signals as they are received by sensors, because scales of the sensor responses (images) have known physical interpretations. In this paper, we analyze two approaches that are widely used for computing the sensor responses, especially, in Frequency-Domain Independent Component Analysis. One approach is the least-squares projection, while the other one assumes a regular mixing matrix and computes its inverse. Both estimators are invariant to the unknown scaling. Although frequently used, their differences were not studied yet. A goal of this work is to fill this gap. The estimators are compared through a theoretical study, perturbation analysis and simulations. We point to the fact that the estimators are equivalent when the separated signal subspaces are orthogonal, and vice versa. Two applications are shown, one of which demonstrates a case where the estimators yield substantially different results.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA14-11898S" target="_blank" >GA14-11898S: Zlepšování řečového signálu pomocí částečně slepých metod za použití pole mikrofonů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

16

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

4166-4176

Kód UT WoS článku

000404286900002

EID výsledku v databázi Scopus

2-s2.0-85020745841