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Publication detail
ŠVENTO, M. BALUŠÍK, P.
Original Title
Deep prior audio compression
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
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.
Keywords
audio processing; deep learning; deep audio prior; compression
Authors
ŠVENTO, M.; BALUŠÍK, P.
Released
23. 4. 2024
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno
ISBN
2788-1334
Periodical
Proceedings II of the Conference STUDENT EEICT
State
Czech Republic
Pages count
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" }