论文标题
变形金刚和凹槽建模的吉他选项卡的自动组成
Automatic Composition of Guitar Tabs by Transformers and Groove Modeling
论文作者
论文摘要
深度学习算法越来越开发用于学习以MIDI文件的形式创作音乐。但是，这种算法是否可以很好地构成与MIDIS完全不同的吉他选项卡，但仍未探索。为了解决这个问题，我们构建了一个模型，用于使用Transformer-XL（神经序列模型架构）组成手指型吉他选项卡。使用此模型，我们研究了以下研究问题。首先，神经网是否生成具有有意义的音符组合组合的音符序列，这对吉他很重要，但诸如钢琴等其他乐器都不重要。其次，它是否具有连贯的节奏凹槽的作品，对手指型吉他音乐至关重要。最后，与真实的，人造的作品相比，创作的音乐有多愉快。我们的工作提供了对标签构图深入学习的承诺的初步经验证据，并为未来的研究提供了领域。
Deep learning algorithms are increasingly developed for learning to compose music in the form of MIDI files. However, whether such algorithms work well for composing guitar tabs, which are quite different from MIDIs, remain relatively unexplored. To address this, we build a model for composing fingerstyle guitar tabs with Transformer-XL, a neural sequence model architecture. With this model, we investigate the following research questions. First, whether the neural net generates note sequences with meaningful note-string combinations, which is important for the guitar but not other instruments such as the piano. Second, whether it generates compositions with coherent rhythmic groove, crucial for fingerstyle guitar music. And, finally, how pleasant the composed music is in comparison to real, human-made compositions. Our work provides preliminary empirical evidence of the promise of deep learning for tab composition, and suggests areas for future study.
