  Deep neural networks with convolutional layers usually process the entire
spectrogram of an audio signal with the same time-frequency resolutions, number
of filters, and dimensionality reduction scale. According to the constant-Q
transform, good features can be extracted from audio signals if the low
frequency bands are processed with high frequency resolution filters and the
high frequency bands with high time resolution filters. In the spectrogram of a
mixture of singing voices and music signals, there is usually more information
about the voice in the low frequency bands than the high frequency bands. These
raise the need for processing each part of the spectrogram differently. In this
paper, we propose a multi-band multi-resolution fully convolutional neural
network (MBR-FCN) for singing voice separation. The MBR-FCN processes the
frequency bands that have more information about the target signals with more
filters and smaller dimentionality reduction scale than the bands with less
information. Furthermore, the MBR-FCN processes the low frequency bands with
high frequency resolution filters and the high frequency bands with high time
resolution filters. Our experimental results show that the proposed MBR-FCN
with very few parameters achieves better singing voice separation performance
than other deep neural networks.
