  While deep learning has received a surge of interest in a variety of fields
in recent years, major deep learning models barely use complex numbers.
However, speech, signal and audio data are naturally complex-valued after
Fourier Transform, and studies have shown a potentially richer representation
of complex nets. In this paper, we propose a Complex Transformer, which
incorporates the transformer model as a backbone for sequence modeling; we also
develop attention and encoder-decoder network operating for complex input. The
model achieves state-of-the-art performance on the MusicNet dataset and an
In-phase Quadrature (IQ) signal dataset.
