论文标题
对抗和非对抗性LSTM音乐生成模型的比较
Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models
论文作者
论文摘要
算法音乐作品是一种用最少或没有人为干预的音乐作品的方式。传统上，复发性神经网络被应用于许多序列到序列预测任务，包括成功实现音乐构图，但其基于输入输出映射的标准监督学习方法导致缺乏音符。因此，这些模型可以看作可能不适合音乐发电等任务。生成对抗网络学习数据的生成分布并导致不同的样本。这项工作实施并比较了MIDI数据上复发性神经网络音乐作曲家的对抗性和非对抗性培训。由此产生的音乐样本由人类听众评估，并记录他们的偏好。评估表明，对抗性训练会产生更美观的音乐。
Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, their preferences recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.
