Bandwidth Extension Of Musical Audio Signals With No Side Information Using Dilated Convolutional Neural Networks

Bandwidth extension has a long history in audio processing. While speech processing tools do not rely on side information, production-ready bandwidth extension tools of general audio signals rely on side information that has to be transmitted alongside the bitstream of the low frequency part, mostly because polyphonic music has a more complex and less predictable spectral structure than speech. This paper studies the benefit of considering a dilated fully convolutional neural network to perform the bandwidth extension of musical audio signals with no side information in the spectral domain. Experimental evaluation using two public datasets, medley-solos-db and gtzan, respectively of monophonic and polyphonic music demonstrate that the proposed architecture achieves state of the art performance.