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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54283
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dc.contributor.authorLikhachov, D.-
dc.contributor.authorPetrovsky, N.-
dc.contributor.authorAzarov, E.-
dc.coverage.spatialМинскen_US
dc.date.accessioned2024-02-16T06:54:37Z-
dc.date.available2024-02-16T06:54:37Z-
dc.date.issued2023-
dc.identifier.citationLikhachov, D. Improving Spatial Resolution of First-order Ambisonics Using Sparse MDCT Representation / D. Likhachov, N. Petrovsky, E. Azarov // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 122–125.en_US
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/54283-
dc.description.abstractThe paper presents a method for improving spatial resolution of first-order ambisonic audio. The method is based on time/frequency decomposition of the audio with subsequent extraction of a directed plane wave from each frequency component. The method develops the basic ideas of high angular resolution planewave expansion (HARPEX) and directional audio coding (DirAC) taking advantage of real valued sparse decomposition. Real-valued frequency components as opposed to complex-valued introduce simpler and more stable direction of arrival estimates, while sparse decomposition introduces an accurate and unified approach to describing sounds of different nature from transient to tonal sounds.en_US
dc.language.isoenen_US
dc.publisherBSUen_US
dc.subjectматериалы конференцийen_US
dc.subjectspatial audioen_US
dc.subjectambisonicsen_US
dc.subjectupmixingen_US
dc.subjectspatial resolutionen_US
dc.subjectsparse representationen_US
dc.subjectFFTen_US
dc.subjectMDCTen_US
dc.titleImproving Spatial Resolution of First-order Ambisonics Using Sparse MDCT Representationen_US
dc.typeArticleen_US
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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