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Тема выпуÑкной квалификационной работы «ÐÐ´Ð°Ð¿Ñ‚Ð¸Ð²Ð½Ð°Ñ Ð³ÐµÐ½ÐµÑ€Ð°Ñ†Ð¸Ñ Ð¼ÑƒÐ·Ñ‹ÐºÐ°Ð»ÑŒÐ½Ð¾Ð³Ð¾ контента Ñ Ð¿Ð¾Ð¼Ð¾Ñ‰ÑŒÑŽ иÑкуÑÑтвенных нейронных Ñетей». Ð’ данной работе раÑÑмотрены различные архитектуры иÑкуÑÑтвенных нейронных Ñетей и подходы генерации музыкального контента Ñ Ð¸Ñ… помощью. Выбран подход и две конкретные архитектуры иÑкуÑÑтвенных нейронных Ñетей. Проведено обучение выбранных иÑкуÑÑтвенных нейронных Ñетей и Ñравнение их результатов. Разработана ÑиÑтема разметки ритмичеÑких риÑунков музыкального материала и упрощенной реализации других архитектур иÑкуÑÑтвенных нейронных Ñетей Ð´Ð»Ñ Ñ€ÐµÑˆÐµÐ½Ð¸Ñ Ð¿Ð¾Ñтавленной задачи.
The subject of this qualification work is «Adaptive music generation with artificial neural networks». In this work we analyze different artificial neural network architectures and approaches to computer music generation. We selected an approach and two specific artificial neural network architectures. We completed learning process of aforementioned artificial neural networks and compared their results. We developed a system for data mark up and easy implementation of other artificial neural networks architectures for completed the set-out task.
tensorflow, machine learning, маÑинное обÑÑение, иÑкÑÑÑÑвеннÑе нейÑоннÑе ÑеÑи, music, artificial neural networks, мÑзÑка, keras.
tensorflow, machine learning, маÑинное обÑÑение, иÑкÑÑÑÑвеннÑе нейÑоннÑе ÑеÑи, music, artificial neural networks, мÑзÑка, keras.
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