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.