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Deep neural networks with convolutional layers usually process the entirespectrogram of an audio signal with the same time-frequency resolutions, numberof filters, and dimensionality reduction scale. According to the constant-Qtransform, good features can be extracted from audio signals if the lowfrequency bands are processed with high frequency resolution filters and thehigh frequency bands with high time resolution filters. In the spectrogram of amixture of singing voices and music signals, there is usually more informationabout the voice in the low frequency bands than the high frequency bands. Theseraise the need for processing each part of the spectrogram differently. In thispaper, we propose a multi-band multi-resolution fully convolutional neuralnetwork (MBR-FCN) for singing voice separation. The MBR-FCN processes thefrequency bands that have more information about the target signals with morefilters and smaller dimentionality reduction scale than the bands with lessinformation. Furthermore, the MBR-FCN processes the low frequency bands withhigh frequency resolution filters and the high frequency bands with high timeresolution filters. Our experimental results show that the proposed MBR-FCNwith very few parameters achieves better singing voice separation performancethan other deep neural networks.

Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation