All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Blind extraction of moving audio source in a challenging environment supported by speaker identification via X-vectors

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00008779" target="_blank" >RIV/46747885:24220/21:00008779 - isvavai.cz</a>

  • Result on the web

    <a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/03/ICASSP2021___BSS_embeddings.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/03/ICASSP2021___BSS_embeddings.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9414331" target="_blank" >10.1109/ICASSP39728.2021.9414331</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Blind extraction of moving audio source in a challenging environment supported by speaker identification via X-vectors

  • Original language description

    We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. The blind part of the method is based on independent vector extraction (IVE) and uses the recently proposed constant separating vector (CSV) mixing model. This model allows for changes of mixing parameters within the processed interval of the mixture, which potentially leads to higher accuracy of SOI estimation. The supervised part of the method concerns a pilot signal, which is related to the SOI and ensures the convergence of the blind method towards the SOI. The pilot is based on robust detection of frames where SOI is dominant via speaker embeddings called X-vectors. Robustness of the detection is achieved through augmentation of the data for the supervised training of the X-vectors. The pilot-supported extraction yields significantly better performance compared to its unsupervised counterpart identifying SOI solely using the initialization.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

  • ISBN

  • ISSN

    1520-6149

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    226-230

  • Publisher name

    IEEE

  • Place of publication

    USA

  • Event location

    Toronto, Canada

  • Event date

    Jan 1, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000704288400046