Warping: Data-driven Mixture Preprocessing to Boost the Performance of Blind Speech Separation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F25%3A00013595" target="_blank" >RIV/46747885:24220/25:00013595 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11226076&utm_source=scopus&getft_integrator=scopus" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11226076&utm_source=scopus&getft_integrator=scopus</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Warping: Data-driven Mixture Preprocessing to Boost the Performance of Blind Speech Separation
Original language description
Blind source separation (BSS) can be used to recover speech signals from mixtures recorded by microphones. However, their performances show significant limitations because of the deviations between the instantaneous mixing model and real audio mixtures transformed into the short-time Fourier domain (STFT). This paper presents a data-driven preprocessing technique called mixture warping, which aims to adjust the mixture to obey the instantaneous model as much as possible. As a proof of concept, we demonstrate its effect on a set of reverberant mixtures of two speakers. Warping implemented through a deep neural network is trained to estimate mixtures ideally modified towards the instantaneous model in the least squares sense. By applying it as a preprocessing stage, it boosts the BSS performance by up to 6.4 dB of signal-to-interference (SIR) and 4.7 dB of signal-to-distortion (SDR) on average without requiring any modification to the BSS methods.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA25-18485S" target="_blank" >GA25-18485S: Hybrid Source Extraction: Synergy of physical, information-theoretical and data-based knowledge</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů