论文标题
使用暹罗卷积神经网络进行封面歌曲检测
Towards Cover Song Detection with Siamese Convolutional Neural Networks
论文作者
论文摘要
从定义上讲，封面歌曲是先前录制的商业发行歌曲的新表演或录制。它可能是由原始艺术家本身或另一个艺术家完全由不可预测的方式而异，包括钥匙，安排，仪器，音色等。在这项工作中，我们提出了一种新颖的方法来学习封面歌曲检测任务的音频表示。我们在数以万计的封面音频剪辑上培训神经建筑，并在固定的设置上进行测试。我们在迷你批次上获得平均精度为65％的1，比随机猜测要好十倍。我们的结果表明，暹罗网络配置显示出接近封面歌曲识别问题的希望。
A cover song, by definition, is a new performance or recording of a previously recorded, commercially released song. It may be by the original artist themselves or a different artist altogether and can vary from the original in unpredictable ways including key, arrangement, instrumentation, timbre and more. In this work we propose a novel approach to learning audio representations for the task of cover song detection. We train a neural architecture on tens of thousands of cover-song audio clips and test it on a held out set. We obtain a mean precision@1 of 65% over mini-batches, ten times better than random guessing. Our results indicate that Siamese network configurations show promise for approaching the cover song identification problem.
