论文标题
学习使用三胞胎损失对音乐曲目进行排名
Learning to rank music tracks using triplet loss
论文作者
论文摘要
大多数音乐流媒体服务都依靠自动推荐算法来利用其大型音乐目录。这些算法旨在根据与目标音乐曲目的相似性来检索音乐曲目的排名列表。在这项工作中，我们提出了一种基于音频内容的直接推荐方法，而无需明确标记音乐曲目。为此，我们提出了几种策略，以从排名列表中执行三重态采矿。我们训练一个卷积神经网络，通过三胞胎损失来学习相似性。这些不同的策略在大规模实验中进行了比较和验证，可针对基于自动标记的方法进行比较。获得的结果突出了我们系统的效率，尤其是在与自动流动层相关联时。
Most music streaming services rely on automatic recommendation algorithms to exploit their large music catalogs. These algorithms aim at retrieving a ranked list of music tracks based on their similarity with a target music track. In this work, we propose a method for direct recommendation based on the audio content without explicitly tagging the music tracks. To that aim, we propose several strategies to perform triplet mining from ranked lists. We train a Convolutional Neural Network to learn the similarity via triplet loss. These different strategies are compared and validated on a large-scale experiment against an auto-tagging based approach. The results obtained highlight the efficiency of our system, especially when associated with an Auto-pooling layer.
