The use of High-Order Sparse Linear Prediction for the Restoration of Archived Audio
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Date
2020-06
Authors
Bisrat, Derebssa
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Publisher
Addis Ababa University
Abstract
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.
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Keywords
Sparse Linear Prediction, Archived Audio, prediction modeling