Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music (EDM) has always been relatively less investigated compared to others. Considering its wide range of applications, we present a Python package for automated EDM audio generation as an infrastructure for MIR for EDM songs, to mitigate the difficulty of acquiring labelled data. It is a convenient tool that could be easily concatenated to the end of many symbolic music generation pipelines. Inside this package, we provide a framework to build professional-level templates that could render a well-produced track from specified melody and chords, or produce massive tracks given only a specific key by our probabilistic symbolic melody generator. Experiments show that our mixes could achieve the same quality of the original reference songs produced by world-famous artists, with respect to both subjective and objective criteria. Our code is accessible in this repository: https://github.com/Gariscat/loopy and the official site of the project is also online https://loopy4edm.com .
翻译：音乐信息检索（MIR）在近年来深度学习的进步中取得了爆炸性的发展。然而，像电子舞曲（EDM）这样的音乐类型在相对较少的调查中。考虑到其广泛的应用，我们提供了一个Python包作为MIR基础设施，用于EDM歌曲的自动化生成，以缓解获取标记数据的困难。这是一个方便的工具，可以轻松连接到许多符号音乐生成管道的末端。在这个包中，我们提供了一个框架来构建专业水平的模板，可以从指定的旋律和和弦渲染出一个制作精良的曲目，或者通过我们的概率符号旋律生成器只给定一个特定的键就可以产生大规模的曲目。实验证明，我们的混音可以以客观和主观标准实现与世界著名艺术家制作的原始参考曲目相同的质量。我们的代码可以在这个存储库中访问：https://github.com/Gariscat/loopy，并且该项目的官方网站也在线上https://loopy4edm.com。
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