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ŠVENTO, M. BALUŠÍK, P.
Originální název
Deep prior audio compression
Typ
článek ve sborníku mimo WoS a Scopus
Jazyk
angličtina
Originální abstrakt
Audio compression is still an up-to-date topic because the demand for big data streams is rapidly increasing. Deep learning has brought up new algorithms that decrease bitrates with good perception quality. The novel approach in generative artificial intelligence is to produce new data from prior stored in network parameters, called a deep prior. The deep audio prior framework shows its success in various tasks such as inpainting, declipping, and bandwidth extension, but it has not been tested for compression. In this paper, we test this method with a pre-built network for inpainting. Our idea of compression is based on reducing the number of time-frequency coefficients in the spectrogram while allowing the reconstruction of the original signal with high quality.
Klíčová slova
audio processing; deep learning; deep audio prior; compression
Autoři
ŠVENTO, M.; BALUŠÍK, P.
Vydáno
23. 4. 2024
Nakladatel
Brno University of Technology, Faculty of Electrical Engineering and Communication
Místo
Brno
ISSN
2788-1334
Periodikum
Proceedings II of the Conference STUDENT EEICT
Stát
Česká republika
Strany počet
5
BibTex
@inproceedings{BUT188830, author="Michal {Švento} and Peter {Balušík}", title="Deep prior audio compression", booktitle="Proceedings I of the 30th Student EEICT 2024 (General Papers)", year="2024", series="1", journal="Proceedings II of the Conference STUDENT EEICT", pages="5", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", issn="2788-1334" }