title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÐ¾Ð² Ð´Ð»Ñ Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Application of autoencoders for recovery of audio signals
Creators: name: ÐÐ¾ÑÐ¾ÑÑÐ¸Ð½, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑ
Ð°Ð´Ð´Ð¸ÑÐ¸Ð²Ð½ÑÐ¹ Ð±ÐµÐ»ÑÐ¹ Ð³Ð°ÑÑÑÐ¾Ð²ÑÐºÐ¸Ð¹ ÑÑÐ¼
ÑÑÐµÐ´Ð½ÐµÐºÐ²Ð°Ð´ÑÐ°ÑÐ¸ÑÐ½Ð°Ñ Ð¾ÑÐ¸Ð±ÐºÐ°
ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°
ÑÑÐ¼Ð¾Ð¿Ð¾Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ðµ
autoencoder
additive white gaussian noise
RMS error
spectrogram
noise reduction

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description: The subject of the graduate qualification work is neural networks with autoencoder architecture. The purpose of the work is to build an autoencoder model for restoring audio signals, to consider the effectiveness of this method. This work contains a study of the possibilities of using an autoencoder to restore a noised audio signal.In the course of the work, a literary review of machine learning methods, the principle of operation of a denoising autoencoder, as well as theoretical information about Gaussian noise was given. The paper shows operations for preliminary data preparation, the principle of constructing an autoencoder model and the use of the created autoencoder for restoring signals of various types. The obtained results are visualized, the pure and reconstructed signals are compared, the autoencoder efficiency is estimated for the problem of distorted signal recovery. As a result of the work, an autoencoder model was built that can suppress additive white Gaussian noise in sinusoidal signals, and the performance of the autoencoder to eliminate noise in complex signals (speech) was considered. The task of restoring audio signals with an autoencoder is a relatively new and rapidly developing area of machine learning. Applications include communication applications, voice assistants, phone call applications and many other applications that use audio signals. The purpose of this work is to develop, apply and evaluate the effectiveness of using an autoencoder to restore distorted audio signals.