Dictionary-Based Sparse Reconstruction of Incomplete Relative Transfer Functions

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00008827" target="_blank" >RIV/46747885:24220/21:00008827 - isvavai.cz</a>
Result on the web
<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/10/Dictionary_Based_Sparse_Reconstruction_of_Incomplete_Relative_Transfer_Functions.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2021/10/Dictionary_Based_Sparse_Reconstruction_of_Incomplete_Relative_Transfer_Functions.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/EUSIPCO54536.2021.9616062" target="_blank" >10.23919/EUSIPCO54536.2021.9616062</a>

Result language
angličtina
Original language name
Dictionary-Based Sparse Reconstruction of Incomplete Relative Transfer Functions
Original language description
For estimating the relative transfer function (RTF) of a speaker from noisy multi-microphone recordings, several statistical methods have been proposed. The estimation accuracy is different over frequencies, which mostly depends on the frequency-dependent signal-to-noise ratio (SNR). Provided that the low-SNR frequencies are identified, the corresponding values of the estimated RTF can be replaced through interpolation using the frequencies with high SNR. In this study, we explore interpolation techniques based on the sparse reconstruction of an incomplete RTF which is obtained when low-SNR values are neglected. Compared to previous attempts where the approximate sparsity of the time-domain representation of RTF (relative impulse response) is exploited, in this paper, we use learned sparse dictionaries trained on dense measurements of RTFs within a confined area of the target speaker. These measurements are obtained from the recently released MIRaGe database acquired in a real room.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA20-17720S" target="_blank" >GA20-17720S: Advanced Mixing Models for Blind Source Extraction</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
European Signal Processing Conference (EUSIPCO 2021)
ISBN
978-908279706-0
ISSN
2219-5491
e-ISSN
—
Number of pages
4
Pages from-to
1005-1009
Publisher name
Eurasip
Place of publication
Ireland
Event location
Dublin
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000764066600199

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