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dc.contributor.authorLikhachov, Denis
dc.contributor.authorPetrovsky, Nick
dc.contributor.authorAzarov, Elias
dc.date.accessioned2023-12-12T12:42:11Z-
dc.date.available2023-12-12T12:42:11Z-
dc.date.issued2023
dc.identifier.citationPattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 122-125.
dc.identifier.isbn978-985-881-522-6
dc.identifier.urihttps://elib.bsu.by/handle/123456789/306213-
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
dc.language.isoen
dc.publisherMinsk : BSU
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.subjectЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.titleImproving Spatial Resolution of First-order Ambisonics Using Sparse MDCT Representation
dc.typeconference paper
Appears in Collections:2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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