UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies

Serra, Arthur C; Busson, Antonio Jose G; Guedes, Alan L; Colcher, Sergio; (2021) Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies. In: WebMedia '21: Proceedings of the Brazilian Symposium on Multimedia and the Web. (pp. pp. 205-211). ACM Green open access

[thumbnail of 2021_Audio_Reconstruction__Copy_Colcher_.pdf] Text
2021_Audio_Reconstruction__Copy_Colcher_.pdf - Other

Download (1MB)

Abstract

Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction-in an approach called audio inpainting. Current state-of-The art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant.

Type: Proceedings paper
Title: Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies
Event: 27th Brazilian Symposium on Multimedia and the Web (WebMedia)
Location: ELECTR NETWORK
Dates: 5 Nov 2021 - 12 Nov 2021
ISBN-13: 9781450386098
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3470482.3479635
Publisher version: https://doi.org/10.1145/3470482.3479635
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Audio quality enhancement, Audio reconstruction, Autoencoder, Computer Science, Computer Science, Interdisciplinary Applications, Computer Science, Software Engineering, Computer Science, Theory & Methods, Neural Networks, Science & Technology, Technology
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10163975
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item