@inproceedings{
 title = {Analysis of compressive sensing for non stationary music signal},
 type = {inproceedings},
 year = {2016},
 keywords = {Coherence,Compressive Sensing (CS),Independent & Identically Distributed (IID),Restricted Isometric Property (RIP),Sparsity},
 id = {0579b40c-c040-33db-b283-95d5f9035df6},
 created = {2018-09-06T11:22:39.988Z},
 file_attached = {false},
 profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},
 last_modified = {2018-09-06T11:22:39.988Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2016 IEEE. Compressive Sensing (CS) is the key method to reconstruct the signal with very few number of measurements as compared to conventional methods. According to the conventional Shannon-Nyquist sampling theory, the signal has to sample twice the bandwidth in order to have the proper reconstruction. It is required to store a large amount of data according to the conventional method. CS helps to resolve this issue with two important parameters such as the measurement matrix and the basis matrix. They should satisfy two properties which are Restricted Isometric Property (RIP) and Independent and Identically Distributed (IID). There are various reconstruction algorithms which are useful for the proper recovery of the signal after applying the CS technique. The work is carried out on different types of audible signals which are non-stationary in the nature. For the single tone audio signal, the value of SNR is quiet good. Whereas the SNR value of the music signal and the instrumental signal has been degraded because of the single tone frequency component. The value of RIP constant varies with the change in number of measurements.},
 bibtype = {inproceedings},
 author = {Joshi, A.M. and Upadhyaya, V.},
 doi = {10.1109/ICACCI.2016.7732203},
 booktitle = {2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016}
}

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According to the conventional Shannon-Nyquist sampling theory, the signal has to sample twice the bandwidth in order to have the proper reconstruction. It is required to store a large amount of data according to the conventional method. CS helps to resolve this issue with two important parameters such as the measurement matrix and the basis matrix. They should satisfy two properties which are Restricted Isometric Property (RIP) and Independent and Identically Distributed (IID). There are various reconstruction algorithms which are useful for the proper recovery of the signal after applying the CS technique. The work is carried out on different types of audible signals which are non-stationary in the nature. For the single tone audio signal, the value of SNR is quiet good. Whereas the SNR value of the music signal and the instrumental signal has been degraded because of the single tone frequency component. The value of RIP constant varies with the change in number of measurements.","bibtype":"inproceedings","author":"Joshi, A.M. and Upadhyaya, V.","doi":"10.1109/ICACCI.2016.7732203","booktitle":"2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016","bibtex":"@inproceedings{\n title = {Analysis of compressive sensing for non stationary music signal},\n type = {inproceedings},\n year = {2016},\n keywords = {Coherence,Compressive Sensing (CS),Independent & Identically Distributed (IID),Restricted Isometric Property (RIP),Sparsity},\n id = {0579b40c-c040-33db-b283-95d5f9035df6},\n created = {2018-09-06T11:22:39.988Z},\n file_attached = {false},\n profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},\n last_modified = {2018-09-06T11:22:39.988Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2016 IEEE. 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Whereas the SNR value of the music signal and the instrumental signal has been degraded because of the single tone frequency component. 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