3 research outputs found
Recurrent neural network model for music generation
No full textThe paper considers the possibility of generating musical compositions using recurrent neural networks. Two approaches to the generation of musical works are proposed and considered, namely using the method of notes and the method of chords. The research of both methods was carried out, and their advantages and disadvantages were formulated. As a result it was decided to use the method of notes as the main one for music generation. The process of searching and processing data for learning a music neural network is describedin detail, the algorithm for converting data from MIDI format to your own text for use in a neural network is considered in detail. The learning process of the neural network was also described, and the learning speed was compared using GPUs and CPUs, as a result of which it was determined that learningtakes place faster using a graphics processor, in some cases 5.5 times. As a result of testing the operation of the neural network, it was determined that the optimal characteristics of the recurrent neural network for music generation is a network consisting of 4 LSTM layers, each with a dimension of 600 neurons. As music generation cannot be assessed by objective characteristics, a special focus group survey was conducted to assess quality. It shows that music generated by a neural network received almost the same marks as music. written by a man. It should be considered as a great result. It was also determined that it was difficult for the survey participants to correctly identify the author of a musical work, since they correctly identified the authors in only 58% of cases. The proposed solution allows to easily generate musical compositions without human intervention. Problems in programming 2022; 1: 87-9
Модель рекурентної нейронної мережі для генерації музики
Get PDFThe paper considers the possibility of generating musical compositions using recurrent neural networks. Two approaches to the generation of musical works are proposed and considered, namely using the method of notes and the method of chords. The research of both methods was carried out, and their advantages and disadvantages were formulated. As a result it was decided to use the method of notes as the main one for music generation. The process of searching and processing data for learning a music neural network is describedin detail, the algorithm for converting data from MIDI format to your own text for use in a neural network is considered in detail. The learning process of the neural network was also described, and the learning speed was compared using GPUs and CPUs, as a result of which it was determined that learningtakes place faster using a graphics processor, in some cases 5.5 times. As a result of testing the operation of the neural network, it was determined that the optimal characteristics of the recurrent neural network for music generation is a network consisting of 4 LSTM layers, each with a dimension of 600 neurons. As music generation cannot be assessed by objective characteristics, a special focus group survey was conducted to assess quality. It shows that music generated by a neural network received almost the same marks as music. written by a man. It should be considered as a great result. It was also determined that it was difficult for the survey participants to correctly identify the author of a musical work, since they correctly identified the authors in only 58% of cases. The proposed solution allows to easily generate musical compositions without human intervention. Problems in programming 2022; 1: 87-93У роботі розглядається можливість генерації музичних композицій, використовуючи рекурентні не- йронні мережі. Запропоновано та розглянуто два методи генерації музичних творів – на рівні нот та на рівні акордів. Проведено дослідження обох методів та визначено їх переваги та недоліки. Далі для розробки обрано метод генерації на рівні нот, для якого детально описано процес пошуку та об- робки даних для навчання генерації музики за допомогою рекурентної нейронної мережі. Це дозволяє автоматизувати генерацію музичних творів без втручання людини. Для побудованої моделі виконано програмну реалізацію запропонованого рішення, проведені експерименти та їх верифікація за участю фокус-групи людей щодо визначення авторства створеної музики – людина чи комп’ютер.Problems in programming 2022; 1: 87-93
Academician А.Yu. Ishlinsky (in Commemoration of the 100th Anniversary since his Birthday)
No full textВыдающемуся ученому в области математики и механики, одному из создателей теории инерциальной навигации и управления, автору фундаментальных исследований в области механики сплошной среды, динамики твердого тела и гироскопии, крупному организатору науки и замечательному педагогу академику Александру Юльевичу Ишлинскому 6 августа 2013 г. исполнилось бы 100 лет.Видатному вченому в галузі математики та механіки, одному з творців теорії інерційної навігації та управління, автору фундаментальних досліджень з механіки суцільного середовища, динаміки твердого тіла та гіроскопії, крупному організатору науки та чудовому педагогу академіку Олександру Юлійовичу Ішлінському 6 серпня 2013 р. виповнилося б 100 років.The article is written in commemoration of the 100th anniversary (on August 6, 2013) of Alexandr Yu. Ishlinsky, an outstanding scientist in mathematics and mechanics, a creator of the theory of inertial navigation and control, the author of basic research in the field of mechanics of continuous medium, dynamics of solid body and hyroscopy, a great organizer of science and a remarkable teacher
