Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Development and Research of Neural Network Based Method for Reconstructing Audio Signals. / Morozova, Kristina; Rakitskiy, Anton.
Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021. Institute of Electrical and Electronics Engineers Inc., 2021. стр. 316-318 9455000 (Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Development and Research of Neural Network Based Method for Reconstructing Audio Signals
AU - Morozova, Kristina
AU - Rakitskiy, Anton
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021/5/13
Y1 - 2021/5/13
N2 - In this paper we investigate the possibility of using neural networks to solve the problem of restoring audio signal. Based on the previously obtained results of the convolutional neural networks application for the extraction of a vocal part, we developed the concept of a convolutional neural network designed to correct distorted audio signal. The paper presents the initial concept of this neural network architecture which, unfortunately, showed unsatisfactory results. Nevertheless, based on the concept of this network, several new neural network architectures were developed specifically focused on recovering a distorted audio signal but the shortcomings of the basic architecture were taken into account. The paper contains descriptions of all these architectures and the results of their application to restore the drummer's part in the musical composition where it was removed. The obtained results show the high potential of convolutional neural networks application for solving such a complex problem as audio signal restoration.
AB - In this paper we investigate the possibility of using neural networks to solve the problem of restoring audio signal. Based on the previously obtained results of the convolutional neural networks application for the extraction of a vocal part, we developed the concept of a convolutional neural network designed to correct distorted audio signal. The paper presents the initial concept of this neural network architecture which, unfortunately, showed unsatisfactory results. Nevertheless, based on the concept of this network, several new neural network architectures were developed specifically focused on recovering a distorted audio signal but the shortcomings of the basic architecture were taken into account. The paper contains descriptions of all these architectures and the results of their application to restore the drummer's part in the musical composition where it was removed. The obtained results show the high potential of convolutional neural networks application for solving such a complex problem as audio signal restoration.
KW - audio signal
KW - machine learning methods
KW - neural networks
KW - regression
KW - signal recovery
UR - http://www.scopus.com/inward/record.url?scp=85113817924&partnerID=8YFLogxK
U2 - 10.1109/USBEREIT51232.2021.9455000
DO - 10.1109/USBEREIT51232.2021.9455000
M3 - Conference contribution
AN - SCOPUS:85113817924
T3 - Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
SP - 316
EP - 318
BT - Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
Y2 - 13 May 2021 through 14 May 2021
ER -
ID: 34128804