title: ÐÐ»Ð¸ÑÐ½Ð¸Ðµ ÑÑÑÑÐºÑÑÑÑ ÑÐµÐ·ÑÐ±Ñ Ð½Ð° Ð¿ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÑÑÑÐ°Ð½Ð¾Ð²ÐºÐ¸ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð²Ð¸Ð½Ñ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: Recognition of "short" audio signals based on neural networks of identification
Creators: name: Ð Ð°ÑÐ¿ÑÑÐ¸Ð½, ÐÐ°Ð½Ð¸Ð¸Ð»
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: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð¸Ðµ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
machine learning
neural networks
audiosignal recognition

description: Ð¢ÐµÐ¼Ð° Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ â Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð¸Ðµ Â«ÐºÐ¾ÑÐ¾ÑÐºÐ¸ÑÂ» Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ±ÑÐµÐºÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾-ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð¸Ðµ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸Ð·ÑÑÐµÐ½Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð½Ð¸Ñ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ¹. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð° ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ð° Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð² Ð¿Ð¾Ð²ÑÐµÐ´Ð½ÐµÐ²Ð½Ð¾Ð¹ ÑÑÐµÐ´Ðµ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð½Ð¾Ð²ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ð¸ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÐ²ÑÑÑÐ¾ÑÐ½Ð¾-ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸.Â Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ð² Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÑÐµÐ´Ðµ Google Collaboratory Ð´Ð»Ñ ÑÐ·ÑÐºÐ° Python 3.10.5. ÐÐ»Ñ Ð¿Ð¾Ð´ÐºÐ»ÑÑÐµÐ½Ð¸Ñ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð³Ð¾ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð° Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ° keras, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ°Ñ ÑÐ°ÑÐ¿Ð°ÑÐ°Ð»Ð»ÐµÐ»Ð¸Ð²Ð°ÑÑ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¿ÑÐ¾ÑÐµÑÑÑ. ÐÐ»Ñ ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð±ÑÐ»Ð° Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð° Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ° librosa, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ°Ñ ÑÑÐ¸ÑÑÐ²Ð°ÑÑ Ð°ÑÐ´Ð¸Ð¾ÑÐ¸Ð³Ð½Ð°Ð»Ñ Ð¸ ÑÑÑÐ¾Ð¸ÑÑ ÑÐ¿ÐµÐºÑÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð´Ð¾Ð±Ð¸ÑÑÑÑ Ð¿Ð¾ÑÑÐ¾ÑÐ½Ð½Ð¾Ð³Ð¾ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ Ð²ÐµÐ»Ð¸ÑÐ¸Ð½Ñ Ð¾ÑÐ¸Ð±ÐºÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ.
description: The topic of this work is Recognition of "short" audio signals based on neural networks of identification. The aim of the work is to study the possibility of recognizing audio signals using a neural network. The object of the work is a parallel model of a convolutional-recurrent neural network. The subject of the work is the recognition of an audio signal using a neural network.In the course of the work, existing methods for recognizing audio signals using neural networks were studied. The relevance of this work in the daily environment was also considered. In the course of the work, a new approach was developed, based on the use of a parallel model of a convolutional recurrent neural network. The work on training the neural network was carried out in the Google Collaboratory software environment for the Python 3.10.5 language. To connect hardware acceleration, the keras library was used, which allows parallelizing computational processes. To recognize and analyze the audio signal, the librosa library was used, which allows reading audio signal and building spectrograms. The algorithms obtained as a result of the work make it possible to achieve a constant decrease in the magnitude of the learning error.