Authors:
Christoph F. Stallmann
and
Andries P. Engelbrecht
Affiliation:
University of Pretoria, South Africa
Keyword(s):
Gramophone Records, Interpolation, Audio Reconstruction, Polynomials, Signal Modelling.
Related
Ontology
Subjects/Areas/Topics:
Audio and Video Quality Assessment
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Multimodal Signal Processing
;
Telecommunications
Abstract:
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.