Optimizing dictionary learning parameters for solving Audio Inpainting problem

Václav Mach, Roman Ozdobinski


Recovering missing or distorted audio signal sam-ples has been recently improved by solving an Audio Inpaintingproblem. This paper aims to connect this problem with K-SVD dictionary learning to improve reconstruction error formissing signal insertion problem. Our aim is to adapt an initialdictionary to the reliable signal to be more accurate in missingsamples estimation. This approach is based on sparse signalsreconstruction and optimization problem. In the paper two staplealgorithms, connection between them and emerging problemsare described. We tried to find optimal parameters for efficientdictionary learning.

Full Text:



W. Etter, “Restoration of a discrete-time signal segment by interpolation based on the left-sided and right-sided autoregressive parameters,” IEEE Transactions on Signal Processing, vol. 44, no. 5, pp. 1124 –1135, May 1996.

P. Rajmic and J. Klimek, “Removing crackle from an LP record via wavelet analysis,” in Proceedings of the 7th international conference on digital audio effects DAFx04, 2004, pp. 100–103. [Online]. Available: http://dafx04.na.infn.it/WebProc/Proc/P 038.pdf

G. Cocchi and A. Uncini, “Subbands audio signal recovering using neural nonlinear prediction,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’01), vol. 2, 2001, pp. 1289 –1292 vol.2.

C. Rodbro, M. Murthi, S. Andersen, and S. Jensen, “Hidden markov model-based packet loss concealment for voice over ip,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 5, pp. 1609 –1623, sept. 2006.

A. Adler, V. Emiya, M. Jafari, M. Elad, R. Gribonval, and M. Plumbley, “Audio inpainting,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 3, pp. 922 –932, March 2012.

M. Aharon, M. Elad, and A. M. Bruckstein, “K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations,” IEEE Transactions on Signal Processing, vol. 54, pp. 4311–4322, 2006.

M. Elad, J. Starck, P. Querre, and D. Donoho, “Simultaneous cartoon and texture image inpainting using morphological component analysis (mca),” Applied and Computational Harmonic Analysis, vol. 19, no. 3, pp. 340–358, 2005.

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, 2010.

B. Olshausen and D. Field, “Natural image statistics and efficient coding,” in Computation in Neural Systems, vol. 7, January 1996, pp. 333–339.

K. Engan, S. O. Aase, and J. H. Husy, “Multi-frame compression: theory and design,” Signal Processing, vol. 80, no. 10, pp. 2121 – 2140, 2000. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0165168400000724

K. Engan, B. D. Rao, and K. Kreutz-Delgado, “Frame design using FOCUSS with method of optimal directions (MOD),” in Proc. NORSIG 1999, Tronheim, Norway, June 1999, pp. 65–69.

J. F. Murray and K. Kreutz-Delgado, “An improved focuss-based learning algorithm for solving sparse linear inverse problems,” in Conference Record of the 35rd Asilomar Conference on Signals, Systems and Computers, November 2001.

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP ’05)., vol. 5, march 2005, pp.

v/293 – v/296 Vol. 5.

A. Gersho and A. Gray, Vector Quantization and Signal Compression, ser. The Kluwer International Series in Engineering and Computer Science. Springer-Verlag GmbH, 1992. [Online]. Available: http: //books.google.cz/books?id=DwcDm6xgItUC

V. Mach and R. Ozdobinski. (2012) Source codes for audio inpainting with dictionary learning. [Online]. Available: http://www.stud.feec.vutbr.cz/∼xmachv00/paper2012/

DOI: http://dx.doi.org/10.11601/ijates.v2i1.34


  • There are currently no refbacks.