论文标题
基于内容的音乐相似性与三胞胎网络
Content-based Music Similarity with Triplet Networks
论文作者
论文摘要
我们探索使用三胞胎神经网络以基于内容的音乐相似性嵌入歌曲的可行性。我们的网络使用歌曲的三胞胎训练，以使同一位艺术家的两首歌彼此嵌入，而不是另一首歌曲的第三首歌。我们比较了两个模型，这些模型是通过选择第三首歌曲的不同方法进行训练的：根据共享类型标签的随机与。我们的实验是使用免费音乐档案中的歌曲进行的，并使用标准音频功能进行。最初的结果表明，浅暹罗网络可用于嵌入音乐以完成简单的艺术家检索任务。
We explore the feasibility of using triplet neural networks to embed songs based on content-based music similarity. Our network is trained using triplets of songs such that two songs by the same artist are embedded closer to one another than to a third song by a different artist. We compare two models that are trained using different ways of picking this third song: at random vs. based on shared genre labels. Our experiments are conducted using songs from the Free Music Archive and use standard audio features. The initial results show that shallow Siamese networks can be used to embed music for a simple artist retrieval task.
