Abstract
There are various data compression techniques available in the literature. But for proper reconstruction of the original signal we have to follow the Shannon theorem. According to the Shannon theorem the sampling rate must be greater than twice of the highest component of that signal. But as we know that there are various applications present nowadays in which we have required lot of data so due to that this is so much tedious to follow the Shannon’s theorem. The solution of this problem is known as Compressive Sensing. It is the method by using which we can reconstruct the signal by using very few components so the problem associated with the Shannon’s theorem can be resolved. In this paper, our main objective is to show the SNR improvement by using the Compressive Sensing (CS) technique. Sound signals of five music instruments are taken into this analysis. We used l1 reconstruction algorithm for the proper reconstruction of the original signal. Gaussian matrix is used as measurement matrix and the DCT is used as the basis matrix. SNR and MSE are the two crucial parameters which are recorded in this analysis. Effect of Compressive Sensing on the five music instrumental signals are analyzed and shown by using two different plots.
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Kumari, P., Sujediya, G., Upadhyaya, V. (2020). Variation Measurement of SNR and MSE for Musical Instruments Signal Compressed Using Compressive Sensing. In: Sharma, H., Pundir, A., Yadav, N., Sharma, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0426-6_13
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DOI: https://doi.org/10.1007/978-981-15-0426-6_13
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