Analysis of compressive sensing for non stationary music signal

Joshi, Amit M.; Upadhyaya, Vivek

doi:10.1109/icacci.2016.7732203

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The main objective of MSE is to find out the distortion level between the actual and reconstructed signal. The relation for MSE between actual & reconstructed signal is given below [19].…”

Section: A Mean Square Error (Mse)mentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Upadhyaya

Salim

2020

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“…The main objective of MSE is to find out the distortion level between the actual and reconstructed signal. The relation for MSE between actual & reconstructed signal is given below [19].…”

Section: A Mean Square Error (Mse)mentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Upadhyaya

Salim

2020

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“…Vivek Upadhyaya and Amit M Joshi [8] proposed some work on the compressive sensing. They put emphasis on the compression ratio and the value of RIP constant.…”

Section: Literature Surveymentioning

confidence: 99%

To Study the Effects of Basis and Sensing Matrix on Compressive Sensing

Nibheriya¹,

Upadhyaya²,

Kajla³

2018

The Oxford Journal of Intelligent Decision and Data Science

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Compressive sensing (CS) is the appropriate way to recover the compressible signal with very few observations or precisely very little number of measurements rather than the conventional methods. As per the theory given by Shannon for proper recovery of a signal the sampling frequency must be greater than or equal to the largest frequency component in that signal. So the storage requirement to store the data according to the Nyquist theorem is too high. So compressive sensing is used to reduce this storage requirement. There are two important parameters one is sensing matrix and another is measurement matrix by changing these two parameters we can change the quality of the recovered signal. There are various reconstruction algorithms which are used for proper reconstruction of signal. The work which is done in this paper comprises of various music signals on which the compressive sensing applied. As per the result the single tone music signal have less value of MSE than the multi tone and vocal song signal. The SNR value is quiet good for single tone than the multi tone & vocal song. This is due to the single frequency component in the single tone music signal.

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Comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression

Swami

Sharma

2022

Communications in Computer and Information Science

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Analysis of compressive sensing for non stationary music signal

Cited by 7 publications

References 11 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

To Study the Effects of Basis and Sensing Matrix on Compressive Sensing

Comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression

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