Detail publikace

Deep prior audio compression

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"
}