Toggle Main Menu Toggle Search

Open Access padlockePrints

Audio Super-Resolution Using Analysis Dictionary Learning

Lookup NU author(s): Professor Jonathon Chambers


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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.

Publication metadata

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

Pages: 604-608

Print publication date: 01/01/2015

Online publication date: 10/09/2015

Acceptance date: 01/01/1900

ISSN: 1546-1874

Publisher: IEEE


DOI: 10.1109/ICDSP.2015.7251945

Library holdings: Search Newcastle University Library for this item

ISBN: 9781479980598