Supervised Independent Vector Analysis Through Pilot Dependent Components

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F17%3A00004532" target="_blank" >RIV/46747885:24220/17:00004532 - isvavai.cz</a>

Výsledek na webu

<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2017/03/icassp2017.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2017/03/icassp2017.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2017.7952213" target="_blank" >10.1109/ICASSP.2017.7952213</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Supervised Independent Vector Analysis Through Pilot Dependent Components

Popis výsledku v původním jazyce

Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional source representation and act as a prior knowledge. The resulting adaptation still maximizes the multivariate source independence, while simultaneously forcing the estimation of sources dependent on the pilot components. It is also shown as the S-IVA is a generalization over the previously proposed weighted Natural Gradient. Numerical evaluations shows the effectiveness of the proposed method in challenging real-world applications.

Název v anglickém jazyce

Supervised Independent Vector Analysis Through Pilot Dependent Components

Popis výsledku anglicky

Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional source representation and act as a prior knowledge. The resulting adaptation still maximizes the multivariate source independence, while simultaneously forcing the estimation of sources dependent on the pilot components. It is also shown as the S-IVA is a generalization over the previously proposed weighted Natural Gradient. Numerical evaluations shows the effectiveness of the proposed method in challenging real-world applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE International Conference on Audio, Speech, and Signal Processing 2017

ISBN

978-1-5090-4117-6

ISSN

15206149

e-ISSN

—

Počet stran výsledku

5

Strana od-do

536-540

Název nakladatele

—

Místo vydání

New Orleans, USA

Místo konání akce

New Orleans, USA

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000414286200108