风格特定的土耳其流行音乐作曲与CNN和LSTM网络

Senem Tanberk, D. Tükel
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引用次数: 7

摘要

人工神经网络的最新进展为自动音乐生成提供了灵感。深度学习算法有助于产生悦耳的旋律。他们引导音乐家的创造力在数字环境中得到再现。该系统从土耳其流行音乐中学习,然后产生新的音乐。在这项研究中,我们的目标是生成具有特定风格的旋律,例如广受赞赏的令人难忘的原声。我们提出了一种卷积神经网络(CNN)和长短期记忆(LSTM)网络相结合的音乐生成方法。实验结果表明,与仅lstm深度模型或仅cnn深度模型相比,所提出的组合深度模型具有显著的音乐质量。我们还进行了一项调查,以评估生成的音乐的质量。调查结果表明,与其他最先进的音乐生成方法相比,所引入的模型能够产生更好的质量和更令人愉快的音乐。
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Style-Specific Turkish Pop Music Composition with CNN and LSTM Network
The recent advance in artificial neural networks is an inspiration for automatic music generation. Deep learning algorithms help to produce pleasing melodies. They lead the creativity of musicians to be reproduced in digital environments. The proposed system learns from the Turkish popular music and then produces new music. In this study, our goal is to generate melody with a specific style, such as unforgettable soundtracks admired widely. We proposed a novel combination of convolutional neural network (CNN) and long short-term memory (LSTM) network for music generation. The experimental results reveal that the proposed combined deep model exhibits remarkable music quality compared to the lstm-only deep model or cnn-only deep model. We also conducted a survey to evaluate the quality of the generated music. The survey results show that the introduced model is capable of producing better quality and more pleasant music compared to other state-of-the-art music generation methods.
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