The use of High-Order Sparse Linear Prediction for the Restoration of Archived Audio

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Addis Ababa University


Since the invention of Gramophone by Thomas Edison in 1877, vast amounts of cultural, entertainment, educational and historical audio recordings have been recorded and stored throughout the world. Through natural aging and improper storage, the recorded signal degrades and loses its information in terms of quality and intelligibility. Degradation of audio signals is considered as any unwanted modi cation to the audio signal after it has been recorded. There are di erent degradations a ecting recorded signals on analog storage media. The degradations that are often encountered are clicks, hiss and `Wow and Flutter'. Several researches have been conducted in restoring degraded audio recordings. Most of the methods rely on some prior information of the underlying data and the degradation process. The success of these methods heavily depends on the prior information available. When such information is not available, a model of the underlying undegraded data can be used to generate such prior information. Linear prediction is one of the most widely used models to represent speech. However, linear prediction has limitations for voiced speech and music and as such restoration approaches that use linear prediction have limited success for voiced speech and music. This research uses recent ndings in linear prediction modeling in the restoration of click and `wow and utter'. Recent developments in e cient algorithms and computational capability have led to signi cant investigations on the usefulness of `1-norm and `0-norm regularization in the solution to the least squares problem. The use of high-order sparse linear prediction for overcoming the limitations posed by conventional linear prediction has been investigated by other researchers. This research investigates the use of highorder sparse linear prediction for the detection and restoration of degraded archived audio signals. A method is developed that uses the high-order sparse linear prediction model to estimate the underlying audio signal without priori information on the type of audio and the details of the degradation. The model is then used for the detection of the degradations as well as for the restoration of the degraded sample values. The use of the model for two of the most widely encountered degradations in archived audio is investigated.



Sparse Linear Prediction, Archived Audio, prediction modeling