论文标题
metasid：具有域名的域名的歌手识别
MetaSID: Singer Identification with Domain Adaptation for Metaverse
论文作者
论文摘要
Metaverse将现实世界扩展到无限的空间。 Metaverse将有更多现场音乐会。歌手身份证的任务是识别歌曲属于哪个歌手。但是，歌手身份证明是一个棘手的问题，这是不同的现场效果。录音室版本与实时版本不同，训练集的数据分布和测试集不同，分类器的性能也会降低。本文提出了使用域适应方法来解决歌手识别中的实时效果。设计了三种结合域适应性的方法，结合了卷积复发神经网络（CRNN），它们是最大的平均差异（MMD），梯度反转（REVGRAD）和对比度适应网络（CAN）。 MMD是一种基于距离的方法，它增加了域损失。 Revgrad基于以下想法，即学习的功能可以代表不同的域样本。 CAN基于类适应，它考虑了源域和目标域类别之间的对应关系。 Artist20公共数据集的实验结果表明，CRNN-MMD导致基线CRNN的改善提高了0.14。 CRNN-REVGRAD的表现优于基线0.21。 CRNN-CAN在专辑拆分上以0.83的F1度量值实现了最新的技术。
Metaverse has stretched the real world into unlimited space. There will be more live concerts in Metaverse. The task of singer identification is to identify the song belongs to which singer. However, there has been a tough problem in singer identification, which is the different live effects. The studio version is different from the live version, the data distribution of the training set and the test set are different, and the performance of the classifier decreases. This paper proposes the use of the domain adaptation method to solve the live effect in singer identification. Three methods of domain adaptation combined with Convolutional Recurrent Neural Network (CRNN) are designed, which are Maximum Mean Discrepancy (MMD), gradient reversal (Revgrad), and Contrastive Adaptation Network (CAN). MMD is a distance-based method, which adds domain loss. Revgrad is based on the idea that learned features can represent different domain samples. CAN is based on class adaptation, it takes into account the correspondence between the categories of the source domain and target domain. Experimental results on the public dataset of Artist20 show that CRNN-MMD leads to an improvement over the baseline CRNN by 0.14. The CRNN-RevGrad outperforms the baseline by 0.21. The CRNN-CAN achieved state of the art with the F1 measure value of 0.83 on album split.
