  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.
