通过玩音乐益智游戏生成音乐组曲
Generating Music Medleys via Playing Music Puzzle Games
摘要：
生成音乐混合泳是关于找到一个最佳排列的一组给定的音乐剪辑。为了实现这个目标，我们提出了一个自监督学习任务，称为音乐益智游戏，训练神经网络模型学习音乐中的序列模式。从本质上讲，这样的游戏需要机器正确地排序几个多秒的音乐片段。在训练阶段，我们通过从同一首歌曲中采样多个不重叠的片段对来学习模型，并试图预测给定的片段对是否是连续的，并且是否处于正确的时间顺序。为了测试，我们设计了一些不同难度的益智游戏，其中难度最大的是音乐混合泳，它需要对不同歌曲的片段进行排序。在最先进的Siamese卷积网络的基础上，我们提出了一种改进的架构，该架构学习将从输入片段对计算的帧级相似性得分嵌入到公共空间中，其中可以更容易地识别正确顺序的片段对。我们的研究结果表明，由此产生的模型，被称为相似性嵌入网络（SEN），在不同的游戏，包括音乐拼图，音乐排序，音乐混合泳比竞争模型表现更好。示例结果可以在我们的项目网站https://remyhuang.github.io/DJnet上找到。
英文摘要：
Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple non-overlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
