  In this paper, we study the issue of automatic singer identification (SID) in
popular music recordings, which aims to recognize who sang a given piece of
song. The main challenge for this investigation lies in the fact that a
singer's singing voice changes and intertwines with the signal of background
accompaniment in time domain. To handle this challenge, we propose the KNN-Net
for SID, which is a deep neural network model with the goal of learning local
timbre feature representation from the mixture of singer voice and background
music. Unlike other deep neural networks using the softmax layer as the output
layer, we instead utilize the KNN as a more interpretable layer to output
target singer labels. Moreover, attention mechanism is first introduced to
highlight crucial timbre features for SID. Experiments on the existing artist20
dataset show that the proposed approach outperforms the state-of-the-art method
by 4%. We also create singer32 and singer60 datasets consisting of Chinese pop
music to evaluate the reliability of the proposed method. The more extensive
experiments additionally indicate that our proposed model achieves a
significant performance improvement compared to the state-of-the-art methods.
