A survey on the scope of cloud computing

Agarwal, Parul; Sharma, Dilip Kumar; Varun, V.L.; Venkatesh, Pruthvi Raj; Kanchibhotla, Chaitanya; Ventayen, Randy Joy Magno; Mohiddin, Md. Khaja

doi:10.1016/j.matpr.2021.06.275

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…on the website offered by the system [ 9 ]. With the quick growth of big data technology cloud computing [ 10 , 11 ], AI [ 12 , 13 ], and the Internet, technology has profoundly changed people's emotional perceptions, value orientations, ethical standards, thinking styles, and behavioral habits. Nowadays, it is extremely convenient for people to obtain information from the Internet, but it also brings about the trouble of screening useful information, and the phenomenon of “information overload” appears.…”

Section: Introductionmentioning

confidence: 99%

Music Feature Extraction Method Based on Internet of Things Technology and Its Application

Chen

Kou

Hou

et al. 2022

Computational Intelligence and Neuroscience

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Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.

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