@inproceedings{6cd7cded250d493db3b5dd75cc669e13,
title = "Development and Research of Neural Network Based Method for Reconstructing Audio Signals",
abstract = "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.",
keywords = "audio signal, machine learning methods, neural networks, regression, signal recovery",
author = "Kristina Morozova and Anton Rakitskiy",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021 ; Conference date: 13-05-2021 Through 14-05-2021",
year = "2021",
month = may,
day = "13",
doi = "10.1109/USBEREIT51232.2021.9455000",
language = "English",
series = "Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "316--318",
booktitle = "Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021",
address = "United States",
}