Data reduction for high quality digital audio storage and transmission

Author(s):  
F.J. Rumsey
1986 ◽  
Vol 21 (6) ◽  
pp. 1067-1075 ◽  
Author(s):  
J.K.J. Van Ginderdeuren ◽  
H.J. De Man ◽  
B.J.S. De Loore ◽  
H. Vanden Wigngaert ◽  
A. Delaruelle ◽  
...  

2006 ◽  
Vol 52 (3) ◽  
pp. 909-916 ◽  
Author(s):  
A. Floros ◽  
N.-A. Tatlas ◽  
J. Mourjopoulos
Keyword(s):  

2013 ◽  
Vol 101 (9) ◽  
pp. 1905-1919 ◽  
Author(s):  
Karlheinz Brandenburg ◽  
Christof Faller ◽  
Juergen Herre ◽  
James D. Johnston ◽  
W. Bastiaan Kleijn

1994 ◽  
Vol 04 (01) ◽  
pp. 109-115
Author(s):  
TAKIS KASPARIS ◽  
JOHN LANE

A method for digital restoration of phonograph recordings contaminated by impulsive noise is proposed. Impulses are suppressed by applying median filtering on contaminated signal regions only, thus minimizing distortion of clean passages and loss of high musical frequencies. The algorithm can be implemented on a personal computer equipped with any inexpensive sound board, and thus it can be used for the restoration of damaged records in home collections. In experiments with old recordings the improvement in sound quality was dramatic. The restored audio signal can be archived in digital form on regular computer back-up tapes or on digital audio tapes, or it can be played through the sound board and stored onto an analog recording media such as high-quality cassette tapes.


2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan

Only when the input data is reliable can mathematicalmodels and business intelligence systems for decisionmaking produce accurate and effective outputs. However,data taken from primary sources and gathered in a datamart may contain several anomalies that analysts mustidentify and correct. This research covers the activitiesinvolved in creating a high-quality dataset for businessintelligence and data mining. Three techniques areaddressed to achieve this goal: data validation, whichdetects and reduce anomalies and inconsistencies; datamodification, which enhances the precision and robustnessof learning algorithms; and data reduction, whichproduces a set of data with fewer characteristics andrecords but is just as insightful as the original dataset.


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