Independent component and vector analysis

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F18%3A00006126" target="_blank" >RIV/46747885:24220/18:00006126 - isvavai.cz</a>
Result on the web
<a href="https://www.wiley.com/en-us/Audio" target="_blank" >https://www.wiley.com/en-us/Audio</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/9781119279860.ch13" target="_blank" >10.1002/9781119279860.ch13</a>

Result language
angličtina
Original language name
Independent component and vector analysis
Original language description
This chapter introduces popular tools for the blind separation of audio signals using their independence. We are primarily focused on the frequency‐domain separation using independent component analysis (ICA) and independent vector analysis (IVA). First, the fundamentals of ICA, basic signal models and algorithms are introduced. The application of ICA in the frequency domain leads to the scaling ambiguity and to the permutation problem. To solve these problems, we present several basic solutions. Second, IVA is defined, and the most popular IVA algorithms are introduced. Finally, a practical example of the separation of real‐world recordings is presented where the frequency‐domain ICA and IVA are compared.
Czech name
—
Czech description
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Project
<a href="/en/project/GA17-00902S" target="_blank" >GA17-00902S: Advanded Joint Blind Source Separation Methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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