Music has always been thought of as a "human" endeavor -- when praising a piece of music, we emphasize the composer's creativity and the emotions the music invokes. Because music also heavily relies on patterns and repetition in the form of recurring melodic themes and chord progressions, artificial intelligence has increasingly been able to replicate music in a human-like fashion. This research investigated the capabilities of Jukebox, an open-source commercially available neural network, to accurately replicate two genres of music often found in rhythm games, artcore and orchestral. A Google Colab notebook provided the computational resources necessary to sample and extend a total of sixteen piano arrangements of both genres. A survey containing selected samples was distributed to a local youth orchestra to gauge people's perceptions of the musicality of AI and human-generated music. Even though humans preferred human-generated music, Jukebox's slightly high rating showed that it was somewhat capable at mimicking the styles of both genres. Despite limitations of Jukebox only using raw audio and a relatively small sample size, it shows promise for the future of AI as a collaborative tool in music production.

翻译：音乐一直被视为一种“人类”的创造——在赞美一首乐曲时，我们强调的是作曲家的创造力以及音乐所唤起的感情。然而，由于音乐也高度依赖于重复出现的旋律主题与和弦进行所构成的模式和重复，人工智能已日益能够以类似人类的方式复现音乐。本研究探究了开源商用神经网络Jukebox准确复现节奏游戏中常见的两种音乐风格——艺术核与管弦乐——的能力。借助Google Colab笔记本提供的计算资源，我们对两种风格的共16首钢琴编曲进行了采样与扩展。一份包含精选样本的调查问卷被分发给本地的青年管弦乐团，用以评估人们对AI与人类创作音乐的音乐性感知。尽管人类更偏好人类创作的音乐，但Jukebox获得的略高评分表明它在一定程度上能够模仿这两种风格。尽管Jukebox存在仅使用原始音频且样本量相对较小的局限性，但它展示了AI作为音乐制作协作工具的未来潜力。