Gramophone noise reconstruction - a comparative study of interpolation algorithms for noise reduction

Gramophone records have been the main recording medium for seven decades and regained widespread popularity over the past few years. Records are susceptible to noise caused by scratches and other mishandlings, often making the listening experience unpleasant. This paper analyses and compares twenty different interpolation algorithms for the reconstruction of noisy samples, categorized into duplication and trigonometric approaches, polynomials and time series models. A dataset of 800 songs divided amongst eight different genres were used to benchmark the algorithms. It was found that the ARMA model performs best over all genres. Cosine interpolation has the lowest computational time, with the AR model achieving the most effective interpolation for a limited time span. It was also found that less volatile genres such as classical, country, rock and jazz music is easier to reconstruct than more unstable electronic, metal, pop and reggae audio signals.

[1]  Maciej Niedzwiecki,et al.  Localization of impulsive disturbances in archive audio signals using predictive matched filtering , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Jaerin Cho.  Optimal design in regression and spline smoothing , 2007 .

[3]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[4]  Eugen Slutzky.  Summation of random causes as the source of cyclic processes , 1937 .

[5]  Andries Petrus Engelbrecht,et al.  Gramophone Noise Detection and Reconstruction Using Time Delay Artificial Neural Networks , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Hualou Liang,et al.  Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment , 2000, Biological Cybernetics.

[7]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[8]  Henry Hoffmann,et al.  Managing performance vs. accuracy trade-offs with loop perforation , 2011, ESEC/FSE '11.

[9]  Ryan Kastner,et al.  Arithmetic Optimization Techniques for Hardware and Software Design: Fundamentals of digital arithmetic , 2010 .

[10]  C. Duchon.  Lanczos Filtering in One and Two Dimensions , 1979 .

[11]  Maciej Niedzwiecki,et al.  Smart copying-a new approach to reconstruction of audio signals , 2001, IEEE Trans. Signal Process..

[12]  J. I.  The Design of Experiments , 1936, Nature.

[13]  Bronwyn H Hall,et al.  Estimation and Inference in Nonlinear Structural Models , 1974 .

[14]  D. T. Whiteside,et al.  The mathematical papers of Isaac Newton , 1967 .

[15]  Stig Nord,et al.  Approximation properties of the spline fit , 1967 .

[16]  Harris S. Shultz,et al.  The Function sin x/x. , 1990 .

[17]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[18]  Marcin Ciolek,et al.  Elimination of impulsive disturbances from stereo audio recordings , 2015, 2014 22nd European Signal Processing Conference (EUSIPCO).

[19]  C. G. Broyden.  The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .

[20]  P. Whittle,et al.  Hypothesis-Testing in Time Series Analysis. , 1952 .

[21]  Guillermo Sapiro,et al.  Audio restoration from multiple copies , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.