title: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ñ Ð¼ÐµÐ»Ð¾Ð´Ð¸Ð¸ Ð¿ÐµÑÐ½Ð¸
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: Automatic Song Melody Transciption
Creators: name: Ð¡Ð²ÐµÑÐ½Ð¸ÐºÐ¾Ð², Ð Ð¾Ð¼Ð°Ð½
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: 2021
Subjects: Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ñ Ð²Ð¾ÐºÐ°Ð»Ð°
Ð¸ÐµÑÐ°ÑÑ Ð¸ÑÐµÑÐºÐ°Ñ ÑÐºÑÑÑÐ°Ñ Ð¿Ð¾Ð»ÑÐ¼Ð°ÑÐºÐ¾Ð²ÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
Ð¼ÑÐ·ÑÐºÐ°Ð»ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ
automatic singing transcription
hierarchical hidden semi-Markovian model
musical score model

description: Ð¦ÐµÐ»ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¸ Ð¼ÐµÐ»Ð¾Ð´Ð¸Ð¸ Ð¿ÐµÑÐ½Ð¸ (Ð²Ð¾ÐºÐ°Ð»Ð°) ÑÐ°Ð·Ð½ÑÑ ÐºÐ»Ð°ÑÑÐ¾Ð²: Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¸ÐµÑÐ°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐºÑÑÑÐ¾Ð¹ Ð¿Ð¾Ð»ÑÐ¼Ð°ÑÐºÐ¾Ð²ÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ (HHSMM) Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ ResNet-18. ÐÑÐ» ÑÐ°ÐºÐ¶Ðµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½ Ð¼ÐµÑÐ¾Ð´ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¸ Ð¼ÑÐ·ÑÐºÐ¸ Ð¾Ð±ÑÐµÐ³Ð¾ Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð¸Ð· Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Essentia Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ² ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð². ÐÐ»Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¼ÐµÑÑÐ¸ÐºÐ¸, ÑÑÐ²ÐµÑÐ¶Ð´ÐµÐ½Ð½ÑÐµ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸ÐµÐ¹ MIREX. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð²Ð¾ÑÐ¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ Ð¼ÐµÑÐ¾Ð´ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¸ Ð²Ð¾ÐºÐ°Ð»Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÑÐ·ÑÐºÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ HHSMM. ÐÐ°Ð½Ð½ÑÐµ Ð¾ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ResNet-18 Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ Ð¸Ð· ÑÑÐ°ÑÑÐ¸ Ð°Ð²ÑÐ¾ÑÐ¾Ð² Ð¼ÐµÑÐ¾Ð´Ð°, ÑÐ°Ðº ÐºÐ°Ðº Ð¸Ð¼Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ ÑÐµ Ð¶Ðµ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð¸ Ð´Ð°Ð½Ð½ÑÐµ. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð», ÑÑÐ¾ ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ HHSMM Ð¸ ResNet-18 ÑÐ¿ÑÐ°Ð²Ð¸Ð»Ð¸ÑÑ Ñ Ð·Ð°Ð´Ð°ÑÐµÐ¹ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¸ Ð²Ð¾ÐºÐ°Ð»Ð° Ð»ÑÑÑÐµ, Ð½ÐµÐ¶ÐµÐ»Ð¸ Ð¼ÐµÑÐ¾Ð´ Ð¾Ð±ÑÐµÐ³Ð¾ Ð½Ð°Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ. ÐÑÐ¸ ÑÑÐ¾Ð¼ Ð¼ÐµÑÐ¾Ð´ HHSMM Ð½Ðµ Ð´Ð°Ð» Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ, Ð² ÑÐ¾ Ð²ÑÐµÐ¼Ñ ÐºÐ°Ðº ResNet-18 ÑÐ»ÑÑÑÐ¸Ð» Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ Ð½Ð° 15-20 Ð¿ÑÐ¾ÑÐµÐ½ÑÐ¾Ð².
description: The goal of this work is to compare modern automatic singing transcription methods of different classes: the one based on a hierarchical hidden semi-Markovian model and the other based on a convolutional network with the ResNet-18 architecture. The general-purpose music transcription method of the Essentia library was considered as a baseline to identify the potential benefits of specialized methods. To evaluate these methods, metrics approved by MIREX were used. This work reproduces a vocal transcription method based on HHSMM with a musical score model. The ResNet-18 based method performance was taken from the original publication, since they used the same metrics and dataset to evaluate the method. The experiment showed that the specialized methods HHSMM and ResNet-18 coped with the task of vocal transcription better than the general-purpose method. At the same time, the HHSMM method did not yield a significant improvement, while ResNet-18 improved performance by 15-20 percent.