论文标题
音乐音频与自我监督的卷积神经网络相似
Musical Audio Similarity with Self-supervised Convolutional Neural Networks
论文作者
论文摘要
我们已经构建了音乐相似性搜索引擎，该引擎使视频制作人通过可听音乐摘录进行搜索，以此作为对传统全文搜索的补充。我们的系统通过训练具有三胞胎损失术语和音乐转换的自我监视的卷积神经网络，在大型音乐目录中提出了类似的音轨段。半结构化的用户访谈表明，我们可以成功地以搜索体验质量给专业视频制作人留下深刻的印象，并在用户测试中平均搜索7.8/10的查询轨迹相似。我们相信，该搜索工具将使更自然的搜索体验更容易找到音乐来配乐视频。
We have built a music similarity search engine that lets video producers search by listenable music excerpts, as a complement to traditional full-text search. Our system suggests similar sounding track segments in a large music catalog by training a self-supervised convolutional neural network with triplet loss terms and musical transformations. Semi-structured user interviews demonstrate that we can successfully impress professional video producers with the quality of the search experience, and perceived similarities to query tracks averaged 7.8/10 in user testing. We believe this search tool will make for a more natural search experience that is easier to find music to soundtrack videos with.
