A comparative study of transforms for use in digital audio data compression

2000 ◽  
Vol 108 (1) ◽  
pp. 449-452 ◽  
Author(s):  
B. E. Rulon ◽  
M. F. Shaw ◽  
K. D. Donohue
Keyword(s):  
Comparative Study ◽  
Data Compression ◽  
Digital Audio ◽  
Audio Data

Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

2019 ◽  
Vol 11 (2) ◽  
pp. 47-62 ◽  
Author(s):  
Xinchao Huang ◽  
Zihan Liu ◽  
Wei Lu ◽  
Hongmei Liu ◽  
Shijun Xiang
Keyword(s):  
Time Complexity ◽  
State Of The Art ◽  
Digital Audio ◽  
Activity Detection ◽  
Research Focus ◽  
Audio Forensics ◽  
Significant Research ◽  
Audio Data ◽  
Adjacent Segments ◽  
Voice Activity

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.

RNN-based signal classification for hybrid audio data compression

Computing ◽  
2019 ◽  
Vol 102 (3) ◽  
pp. 813-827 ◽  
Author(s):  
Weiping Tu ◽  
Yuhong Yang ◽  
Bo Du ◽  
Wanzhao Yang ◽  
Xiong Zhang ◽  
...  
Keyword(s):  
Data Compression ◽  
Signal Classification ◽  
Audio Data
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