Open AccessQuality Parameter Index Estimation for Compressive Sensing Based Sparse Audio Signal Reconstruction
Author(s) -
Vivek Upadhyaya,
Gajendra Sharma,
Ajai Kumar,
Sandeep Vyas,
Mohammad Salim
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1119/1/012005
Subject(s) - compressed sensing , computer science , signal (programming language) , nyquist rate , nyquist–shannon sampling theorem , signal reconstruction , noise (video) , transmission (telecommunications) , sampling (signal processing) , compression ratio , compression (physics) , data compression ratio , signal to noise ratio (imaging) , algorithm , signal processing , image compression , artificial intelligence , computer vision , telecommunications , detector , image (mathematics) , image processing , engineering , physics , radar , thermodynamics , programming language , automotive engineering , internal combustion engine
As we, all know that the size of data is increasing tremendously day by day. In a recent project, several petabytes were used to save an image of the Black Hole. Therefore, it is very crucial to develop a method that can reduce the size of data for transmission & storage purposes. The Traditional method for data compression & reconstruction requires so much data space, due to this problem another technique is proposed for the compression and recovery purpose. This method is termed Compressive Sensing (CS). As per the Nyquist sampling theorem, for proper reconstruction of the signal, we have to do sampling at the double rate of maximum data rate available in the signal. As a result, the storage requirement increased as well as the cost of the system was also enhanced. While on the other hand in Compressive Sensing, little samples are required for the reconstruction of the signal. So here in this paper, we have considered three music signals which are single tone, instrumental and vocal song. Values of Mean Square Error, Root Mean Square Error and Signal to Noise Ratio for different compression ratios mentioned in the tables and plots. By analyzing these values we can easily investigate the effectiveness of compressive sensing.