A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings

Eloi Moliner*, Vesa Välimäki

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

11 Sitaatiot (Scopus)
62 Lataukset (Pure)

Abstrakti

Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural network. A two-stage U-Net model architecture is designed to model and suppress the degradations with high fidelity. The method processes the time-frequency representation of audio, and is trained using realistic noisy data to jointly remove hiss, clicks, thumps, and other common additive disturbances from old analog discs. The proposed model outperforms previous methods in both objective and subjective metrics. The results of a formal blind listening test show that real gramophone recordings denoised with this method have significantly better quality than the baseline methods. This study shows the importance of realistic training data and the power of deep learning in audio restoration.

AlkuperäiskieliEnglanti
Otsikko2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
KustantajaIEEE
Sivut841-845
Sivumäärä5
ISBN (elektroninen)978-1-6654-0540-9
DOI - pysyväislinkit
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Singapore, Singapore
Kesto: 23 toukok. 202227 toukok. 2022

Julkaisusarja

Nimi Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
Maa/AlueSingapore
KaupunkiSingapore
Ajanjakso23/05/202227/05/2022

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