  Most singer identification methods are processed in the frequency domain,
which potentially leads to information loss during the spectral transformation.
In this paper, instead of the frequency domain, we propose an end-to-end
architecture that addresses this problem in the waveform domain. An encoder
based on Multi-scale Dilated Convolution Neural Networks (MDCNN) was introduced
to generate wave embedding from the raw audio signal. Specifically, dilated
convolution layers are used in the proposed method to enlarge the receptive
field, aiming to extract song-level features. Furthermore, skip connection in
the backbone network integrates the multi-resolution acoustic features learned
by the stack of convolution layers. Then, the obtained wave embedding is passed
into the following networks for singer identification. In experiments, the
proposed method achieves comparable performance on the benchmark dataset of
Artist20, which significantly improves related works.
