We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections. To do so, we use models that extract the tempo of songs to make a music playlist follow a narrative arc. Our work specifies an open-source tool that uses pre-trained neural network models to extract the global tempo of a set of raw audio files and applies these measures to create a narrative-following playlist. This tool is available at https://github.com/dylanashley/playlist-story-builder/releases/tag/v1.0.0
翻译:我们研究如何利用在独立媒体集集中产生项目属性的机器学习技术自动将故事嵌入这些收藏中。 为此,我们使用提取歌曲节奏的模型来制作音乐播放列表,遵循叙事弧。我们的工作指定了一个开放源码工具,使用预先培训的神经网络模型来提取一组原始音频文件的全球节奏,并运用这些措施创建一个叙述性播放列表。这个工具可在https://github.com/dylanashley/playlist-story-buildinger/releases/tag/v1.0.0上查阅。