Audio restoration from multiple copies

A method for removing impulse noise from audio signals by fusing multiple copies of the same recording is introduced in this paper. The proposed algorithm exploits the fact that while in general multiple copies of a given recording are available, all sharing the same master, most degradations in audio signals are record-dependent. Our method first seeks for the optimal non-rigid alignment of the signals that is robust to the presence of sparse outliers with arbitrary magnitude. Unlike previous approaches, we simultaneously find the optimal alignment of the signals and impulsive degradation. This is obtained via continuous dynamic time warping computed solving an Eikonal equation. We propose to use our approach in the derivative domain, reconstructing the signal by solving an inverse problem that resembles the Poisson image editing technique. The proposed framework is here illustrated and tested in the restoration of old gramophone recordings showing promising results; however, it can be used in other applications where different copies of the signal of interest are available and the degradations are copy-dependent.

[1]  Michael Vorländer,et al.  Handbook of signal processing in acoustics , 2008 .

[2]  Peter J. W. Rayner,et al.  Digital Audio Restoration: A Statistical Model Based Approach , 1998 .

[3]  Antoine Liutkus,et al.  Separation of Music+Effects Sound Track from Several International Versions of the Same Movie , 2010 .

[4]  Pierre Leveau,et al.  Geometric multichannel common signal separation with application to music and effects extraction from film soundtracks , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Maciej Niedzwiecki,et al.  Smart copying-a new approach to reconstruction of audio signals , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[6]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[7]  P. Lions,et al.  Some Properties of Viscosity Solutions of Hamilton-Jacobi Equations. , 1984 .

[8]  Gonzalo Mateos,et al.  Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization , 2011, IEEE Transactions on Signal Processing.

[9]  Raymond N. J. Veldhuis,et al.  Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes , 1986, IEEE Trans. Acoust. Speech Signal Process..

[10]  Aly A. Farag,et al.  MultiStencils Fast Marching Methods: A Highly Accurate Solution to the Eikonal Equation on Cartesian Domains , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[12]  Xabier Jaureguiberry,et al.  Convolutive common audio signal extraction , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[13]  John Wright,et al.  RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Takis Kasparis,et al.  Adaptive scratch noise filtering , 1993 .

[15]  Michael Elad,et al.  Audio Inpainting , 2012, IEEE Transactions on Audio, Speech, and Language Processing.