Lookup NU author(s): Professor Jonathon Chambers
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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.
Author(s): Dong J, Wang WW, Chambers J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2015 IEEE International Conference on Digital Signal Processing (DSP)
Year of Conference: 2015
Print publication date: 01/01/2015
Online publication date: 10/09/2015
Acceptance date: 01/01/1900
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