title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÑÐµÐ´ÑÑÐ² ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾Ð´Ð½Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾ Ð·Ð²ÑÑÐ°ÑÐ¸Ñ Ð³Ð¾Ð»Ð¾Ñ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: Development of a software system for speech separation
Creators: name: Ð¨Ð°ÑÐ¼Ð¸Ð½, ÐÐ³Ð¾Ñ
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: 2020
Subjects: Ð°ÑÐ´Ð¸Ð¾-Ð²Ð¸Ð·ÑÐ°Ð»ÑÐ½Ð¾Ðµ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐµÑÐ²Ð¸Ñ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð²
Ð¿Ð°ÑÐ°Ð´Ð¸Ð³Ð¼Ð° Ð¾ÑÐµÑÐµÐ´Ð¸ ÑÐ¾Ð¾Ð±ÑÐµÐ½Ð¸Ð¹
ÐºÐ¾Ð½ÑÐµÐ¹Ð½Ð¸ÑÐ¸Ð·Ð°ÑÐ¸Ñ
audio-visual speech separation
deep neural networks
software system for speech separation
message queue paradigm
containerization

description: ÐÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐµÑÐµÐ²ÑÑ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², ÐºÐ¾ÑÐ¾ÑÐ¾Ðµ Ð¼Ð¾Ð¶ÐµÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑÑÑ Ð²Ð¾ Ð¼Ð½Ð¾Ð³Ð¸Ñ Ð¾Ð±Ð»Ð°ÑÑÑÑ, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, Ð² ÑÐ»ÑÑÐ¾Ð²ÑÑ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ°Ñ Ð¸Ð»Ð¸ Ð² Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸ÑÑ Ð¿Ð¾ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ Ð°ÑÐ´Ð¸Ð¾- Ð¸ Ð²Ð¸Ð´ÐµÐ¾Ð´Ð°Ð½Ð½ÑÑ. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¾Ð¿Ð¸ÑÐ°Ð½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ ÑÐµÑÐµÐ½Ð¸Ñ ÑÑÐ¾Ð¹ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ: ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ CASA Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð¸Ñ Ð´Ð¾ÑÑÐ¾Ð¸Ð½ÑÑÐ²Ð° Ð¸ Ð½ÐµÐ´Ð¾ÑÑÐ°ÑÐºÐ¸. ÐÑÐ±ÑÐ°Ð½ Ð¿Ð¾Ð´ÑÐ¾Ð´, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð³Ð»ÑÐ±Ð¾ÐºÐ¸Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÐµÐ¹ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð²ÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð°ÑÐ´Ð¸Ð¾- Ð¸ Ð²Ð¸Ð·ÑÐ°Ð»ÑÐ½ÑÐµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸. Ð¡ÑÐ¾ÑÐ¼ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸Ñ Ðº Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸ Ðº ÑÐµÑÐ²Ð¸ÑÑ, Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÑÑÑÐµÐ³Ð¾ Ð´Ð¾ÑÑÑÐ¿ Ðº Ð½ÐµÐ¹. ÐÐ¿Ð¸ÑÐ°Ð½ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿Ð°ÑÐ°Ð´Ð¸Ð³Ð¼Ñ Ð¾ÑÐµÑÐµÐ´Ð¸ ÑÐ¾Ð¾Ð±ÑÐµÐ½Ð¸Ð¹ Ð¸ ÐºÐ¾Ð½ÑÐµÐ¹Ð½Ð¸ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð°ÑÑÑÐ°Ð±Ð¸ÑÑÐµÐ¼Ð°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° ÑÐµÑÐ²Ð¸ÑÐ°, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð²Ð°ÐµÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÑ Ð²ÑÐ¾Ð´ÑÑÐ¸Ñ Ð·Ð°Ð¿ÑÐ¾ÑÐ¾Ð² Ðº Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾ÑÐµÐ½Ð¾ÑÐ½ÑÑ Ð¼ÐµÑÑÐ¸Ðº Ð¸ Ð¼ÐµÐ´Ð¸Ð°Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾Ð³Ð»Ð°ÑÐ½Ð¾ ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½Ð°ÑÑÐ¸Ð»Ð°ÑÑ ÑÐ¾ÑÐ¾ÑÐ¾ Ð²ÑÐ´ÐµÐ»ÑÑÑ Ð¾Ð´Ð¸Ð½ ÑÐµÑÐµÐ²Ð¾Ð¹ ÑÐ¸Ð³Ð½Ð°Ð» Ð¸Ð· Ð·Ð²ÑÐºÐ¾Ð²Ð¾Ð¹ ÑÐ¼ÐµÑÐ¸. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð½Ð°Ð³ÑÑÐ·Ð¾ÑÐ½Ð¾Ðµ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÑÐ²Ð¸ÑÐ° Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐµÐ³Ð¾ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÑÑÐ°Ð±Ð¸Ð»ÑÐ½Ð¾ÑÑÐ¸.
description: Graduation qualification work is devoted to solve the problem of speech separation. Solution of this problem can be useful for hearing aids or in audio and video processing applications. In this paper, the basic approaches solving this problem are described, namely traditional CASA algorithms and algorithms based on deep learning. Their advantages and disadvantages are analyzed. The approach based on deep neural networks using audio and visual data as input data is chosen. The requirements to the neural network model and the service providing access to the model are formulated. The model training approach is described. A scalable service architecture is developed and implemented with message queue paradigm and containerization. This architecture provides efficient processing of incoming requests to the neural network model. Model results are analyzed using evaluation metrics and media data, according to which the model has learned to predict speech of one speaker from the sound mix. Service stress testing is performed to assess its performance and stability.