Gramophones were the main audio recording medium for more than seven decades and regained widespread popularity over the past few years. Being an analogue storage medium, gramophone records are subject to distortions caused by scratches, dust particles, high temperatures, excessive playback and other noise induced by mishandling the record. Due to the early adoption of the compact disc and other digital audio mediums, most research to reduce the noise on gramophone records focused on physical improvements such as the enhancements of turntable hardware, amelioration of the record material or advances through better record cutting techniques. Comparatively little research has been conducted to digitally filter and reconstruct distorted gramophone recordings.
This thesis provides an extensive analysis on the digital detection and reconstruction of noise in gramophone audio signals distorted by scratches. The ability to approximated audio signals was examined though an empirical analysis of different polynomials and time series models. The investigated polynomials include the standard, Fourier, Newton, Lagrange, Hermite, osculating and piecewise polynomials. Experiments were also conducted by applying autoregressive, moving average and heteroskedasticity models, such as the AR, MA, ARMA, ARIMA, ARCH and GARCH models. In addition, different variants of an artificial neural network were tested and compared to the time series models. Noise detection was performed using algorithms based on the standard score, median absolute deviation, Mahalanobis distance, nearest neighbour, mean absolute spectral deviation and the absolute predictive deviation method. The reconstruction process employed the examined polynomials and models and also considered adjacent window, mirroring window, nearest neighbour, similarity, Lanczos and cosine interpolation.
The detection and reconstruction algorithms were benchmarked using a dataset of 800 songs from eight different genres. Simulations were conducted using artificially generated and real gramophone noise. The algorithms were compared according to their detection and reconstruction accuracy, the computational time needed and the tradeoff between the accuracy and time.
Empirical analysis showed that the highest noise detection accuracy was achieved with the absolute predictive deviation using an ARIMA model. The predictive outlier detector employing a Jordan simple recurrent artificial neural network was most efficient by achieving the best detection rate in a limited timespan. It was also found that the artificial neural networks reconstructed the audio signals more accurately than the other interpolation algorithms. The AR model was most efficient by achieving the best tradeoff between the execution time and the interpolation error.