Szczegóły publikacji
Opis bibliograficzny
Deep learning based spatial aliasing reduction in beamforming for audio capture / Mateusz GUZIK, Giulio Cengarle, Daniel Arteaga // W: Interspeech 2025 [Dokument elektroniczny] : 17–21 August 2025, Rotterdam, The Netherlands. — Wersja do Windows. — Dane tekstowe. — [France : ISCA], [2025]. — ( Interspeech : proceedings of the ... Annual Conference of the International Speech Communication Association ; ISSN 2958-1796 ). — S. 2515-2519. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 2519, Abstr.
Autorzy (3)
- AGHŻbik Mateusz
- Cengarle Giulio
- Arteaga Daniel
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 163191 |
|---|---|
| Data dodania do BaDAP | 2025-10-03 |
| Tekst źródłowy | URL |
| DOI | 10.21437/Interspeech.2025-746 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Interspeech 2025 |
| Czasopismo/seria | Interspeech |
Abstract
Spatial aliasing affects spaced microphone arrays, causing directional ambiguity above certain frequencies, degrading spatial and spectral accuracy of beamformers. Given the limitations of conventional signal processing and the scarcity of deep learning approaches to spatial aliasing mitigation, we propose a novel approach using a U-Net architecture to predict a signal-dependent de-aliasing filter, which reduces aliasing in conventional beamforming for spatial capture. Two types of multichannel filters are considered, one which treats the channels independently and a second one that models cross-channel dependencies. The proposed approach is evaluated in two common spatial capture scenarios: stereo and first-order Ambisonics. The results indicate a very significant improvement, both objective and perceptual, with respect to conventional beamforming. This work shows the potential of deep learning to reduce aliasing in beamforming, leading to improvements in multi-microphone setups.
