Audio Super-Resolution Using Analysis Dictionary Learning

Dong, J; Wang, WW; Chambers, J

doi:10.1109/ICDSP.2015.7251945

Audio Super-Resolution Using Analysis Dictionary Learning

Lookup NU author(s): Professor Jonathon Chambers

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Abstract

Super-resolution is an important problem in signal processing. It aims to reconstruct a high-resolution (HR) signal from a low-resolution (LR) input. We consider the super-resolution problem for audio signals in the time-frequency domain and propose a method using analysis dictionary learning. The input to our proposed method is the LR spectrogram matrix of an audio signal, where some rows corresponding to high-frequency information are lost. First, an analysis dictionary is learned from the spectrogram of some related audio signals. The learned dictionary is then applied in an l(1)-norm regularization term for the reconstruction of the HR spectrogram. Experimental results with piano signals demonstrate the advantage of the learned dictionaries in reconstructing HR spectrograms.