title: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð·Ð²ÑÐºÐ¾Ð²Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸
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: Information model of audio data processing
Creators: name: Ð¡Ð°Ð»ÑÐ°Ðº, Ð¡ÑÐ°Ð½Ð¸ÑÐ»Ð°Ð²
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: 2023
Subjects: Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð°ÑÐ´Ð¸Ð¾
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
deep learning audio
artifical language
data classification
information model

description: ÐÐ±ÑÐµÐºÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: Ð°ÑÐ´Ð¸Ð¾ÑÐ°Ð¹Ð»Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¶Ð°Ð½ÑÐ¾Ð².ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾.Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð´Ð¸Ð¿Ð»Ð¾Ð¼Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾, ÐµÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ, ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ.Ð Ð°Ð±Ð¾ÑÐ° ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð¸Ð· Ð²Ð²ÐµÐ´ÐµÐ½Ð¸Ñ, ÑÐµÑÑÑÐµÑ Ð³Ð»Ð°Ð² Ð¸ Ð·Ð°ÐºÐ»ÑÑÐµÐ½Ð¸Ñ.Ð Ð¿ÐµÑÐ²Ð¾Ð¹ Ð³Ð»Ð°Ð²Ðµ Ð¿ÑÐ¸Ð²Ð¾Ð´ÑÑÑÑ Ð¾Ð±ÑÐ¸Ðµ Ð¿Ð¾Ð½ÑÑÐ¸Ñ, ÑÑÐ¾Ð±Ñ Ð¿Ð¾Ð½ÑÑÑ ÑÑÐ¾ ÑÐ°ÐºÐ¾Ðµ Ð·Ð²ÑÐº, Ð²Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¿Ð¾Ð½ÑÑÐ¸Ðµ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð¸ Ð²Ð¸Ð´Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ.ÐÐ¾ Ð²ÑÐ¾ÑÐ¾Ð¹ Ð³Ð»Ð°Ð²Ðµ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¿Ð¾Ð»Ð½Ð°Ñ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ, ÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð² ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ ÐÐµÐ»Ð°.Ð ÑÑÐµÑÑÐµÐ¹ Ð³Ð»Ð°Ð²ÐµÂ  Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼, Ð¿ÑÑÑÐ¼ Ð²Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¸ ÐºÐ¾ÑÑÑÐ¸ÑÐµÐ½ÑÐ¾Ð².Ð ÑÐµÑÐ²ÐµÑÑÐ¾Ð¹ Ð³Ð»Ð°Ð²Ðµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸Ñ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾, ÐµÐ³Ð¾ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ°ÐºÐ»ÑÑÐµÐ½Ð¸Ðµ Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð²ÑÐ²Ð¾Ð´Ñ Ð¿Ð¾ ÑÐ°Ð±Ð¾ÑÐµ.
description: Research object: audio files of various genres.Research subject: models and methods of deep audio learning.The purpose of this bachelorâs thesis is to research the methods of deep learning audio, its software implementation, testing and analysis of the received data.Thesis consists of an introduction, four sections and conclusion.In the first chapter, general concepts are given to understand what sound is, the concept of a spectrogram and types of deep learning models are introduced.The second chapter provides a complete information model of the spectrogram, its transformation into the Mel spectrogram.The third chapter provides optimization of spectrograms by introducing hyperparameters and coefficients.The fourth chapter introduces the architecture of the audio deep learning application, its implementation, and data analysis. The conclusion includes the main results of the work.