scholarly journals Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Jiao
Keyword(s):  
Feature Extraction ◽  
Leading Edge ◽  
Extraction Process ◽  
Waveform Analysis ◽  
Digital Music ◽  
Feature Extraction Algorithm ◽  
Starting Point ◽  
Depth Analysis ◽  
Extraction Algorithm ◽  
Extraction Model

In order to improve the feature extraction effect of digital music and improve the efficiency of music retrieval, this paper combines digital technology to analyze music waveforms, extract music features, and realize digital processing of music features. Taking the extraction of waveform music file features as the starting point, this paper combines the digital music feature extraction algorithm to build a music feature extraction model and conducts an in-depth analysis of the digital music waveform extraction process. In addition, by setting the threshold, the linear difference between the sampling points on both sides of the threshold on the leading edge of the waveform is used to obtain the overthreshold time. From the experimental research results, it can be seen that the music feature extraction model based on digital music waveform analysis proposed in this paper has good results.

scholarly journals A parallel SRM feature extraction algorithm for steganalysis based on GPU architecture

2015 ◽  
Vol 12 (4) ◽  
pp. 1345-1359 ◽  
Author(s):  
Kaizhi Chen ◽  
Chenjun Lin ◽  
Shangping Zhong ◽  
Longkun Guo
Keyword(s):  
Feature Extraction ◽  
Computing Time ◽  
Extraction Process ◽  
Practical Applications ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Occurrence Matrix ◽  
Gpu Architecture ◽  
Matrix Calculation ◽  
High Computational Complexity

The Spatial Rich Model (SRM) generates powerful steganalysis features, but has high computational complexity since it requires calculating tens of thousands of convolutions with image noise residuals. Practical applications dealing with a massive amount of image transferred through the Internet would suffer a long computing time if using CPU. To accelerate the steganalysis, we present a parallel SRM feature extraction algorithm based on GPU architecture. We exploit parallelism of the algorithm, modify the original SRM extraction algorithm and employ some strategies to avoid the disadvantage of its sequentiality. Some OpenCL optimization technologies are also used to accelerate the extraction process, such as convolution unrolling, combined memory access, split-merge strategy for co-occurrence matrix calculation. The experimental results show that the speed of the proposed parallel extraction algorithm for different size images is 25~55 times faster than the original single thread algorithm. In addition, when using AMD GPU HD 6850, our algorithm runs 2~4.2 times faster than using a Intel Quad-core CPU. This indicates our algorithm makes good use of the GPU cores.

Corresponding Feature Extraction Algorithm between Infrared and Visible Images Using MSER

2011 ◽  
Vol 33 (7) ◽  
pp. 1625-1631 ◽  
Author(s):  
Lin Lian ◽  
Guo-hui Li ◽  
Hai-tao Wang ◽  
hao Tian ◽  
Shu-kui Xu
Keyword(s):  
Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Visible Images

A Seismic-Based Feature Extraction Algorithm for Robust Ground Target Classification

2012 ◽  
Vol 19 (10) ◽  
pp. 639-642 ◽  
Author(s):  
Qianwei Zhou ◽  
Guanjun Tong ◽  
Dongfeng Xie ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Feature Extraction ◽  
Target Classification ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Ground Target
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