The Application of Fog Computing and Internet of Things Technology in Music Resource Management Model

Zhang, Ruizhi

doi:10.1109/access.2019.2963199

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…is is mainly reflected in the connection between NetEase cloud's database and users. Zhang [20] designed a fog computing model for the resource management system of music and dance. is model will use Internet of things technology to effectively classify the resources of music and dance.…”

Section: Related Workmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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Music is a common art form in people’s life, and it is closely related to people’s living conditions. Since ancient times, music has been closely related to people’s lives. The music intelligent management system is convenient and user-friendly, and it can meet the demand for music. However, it has major flaws. Collaborative filtering algorithm can achieve the recommendation performance of music intelligent management system, which can recommend the same type of music to users with related preferences. Deep learning technology has developed relatively maturely, and it has been successfully applied in people’s life and production. Deep convolutional neural network (CNN) techniques can extract deeper features than simple CNN techniques. Although there is a weak nonlinear relationship between people’s behavioral characteristics and living habits and the music intelligent management system, the advantage of deep CNN technology is to deal with the nonlinear relationship between large amounts of data. This study uses deep CNN technology to extract the relationship between people’s living habits, living environment, and behavior characteristics and the music intelligent management system. The deep CNN technology helps the music intelligent management system to further realize the active recommendation function of the music intelligent management system. The research results also show that the deep CNN technology has good feasibility and high accuracy in the music intelligent management system. It can well map the relationship between people’s behavioral characteristics and living habits and the music intelligent management system. The deep CNN technology can also realize the active recommendation function of the music intelligent management system. For the prediction of the music intelligent management system, the largest prediction error is only 2.17%. This part of the error is for the prediction of song genres. The prediction errors for the other two features are both within 2%.

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Section: Related Workmentioning

confidence: 99%

Design of the Music Intelligent Management System Based on a Deep CNN

Shang

Shao

2022

Security and Communication Networks

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“…Literature [11] conceived a music resource optimization strategy with edge computing principles as the core logic and conducted inspection tests in several music terminal cases to verify the efficiency of the proposed music resource optimization strategy. Literature [12] builds a fog computing architecture by drawing on the human neural system framework and analyzes the resource contribution system and resource allocation system in the music and dance resource management model, and finally confirms the excellent performance of the proposed music and dance resource library management system in functional simulation experiments and numerical tests, which is characterized by fast response and large transmission capacity. Literature [13] envisions an intelligent guitar model that enables song retrieval from online music databases, and experts and players highly recognize the proposed approach, believing that the form of musical instruments directly connecting to the database satisfies the needs of music creation and instrument playing.…”

Section: Introductionmentioning

confidence: 70%

Construction of a digital library of vocal music resources based on second-order data decomposition algorithm

Wang,

Guo,

Pan

et al. 2024

Applied Mathematics and Nonlinear Sciences

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In this paper, in order to solve the modal aliasing problem of the EMD decomposition algorithm and EEMD decomposition algorithm and optimize the vocal audio data decomposition on the basis of the adaptive VMD algorithm, the EEMD-VMD second-order data decomposition algorithm is proposed to decompose the digitized time series of vocal music resources, and to update the construction of the digital library of vocal music resources. Combined with the decomposition steps of the second-order data decomposition algorithm in the vocal resources digitized library, the overall framework of the vocal resources digitized library is designed and constructed with the main line of vocal resource types and the secondary line of content categories. Design the experimental environment, preprocess the vocal audio signal features, explore the modal number K value size of the EEMD-VMD second-order data decomposition algorithm, analyze the vocal audio denoising performance of the algorithm, and compare the correlation coefficients of the IMF components of the decomposition algorithms with the original audio signals. Explore the second-order data decomposition algorithm’s emotional classification of vocal resources and the performance of vocal signal enhancement, add noise sources, and use subjective and objective evaluation methods to assess the quality of vocal PESQ and STOI after decomposition processing. The EEMD-VMD second-order data decomposition algorithm performs the most prominently in street noise environments. The enhancement results in the case of signal-to-noise ratios of -5, -10, and -15 are 3.59, respectively, 4.36, 4.29, and the mean value of the enhancement performance reaches 4.08. The second-order data decomposition algorithm’s precise processing of vocal resources provides better quality digital resources for the construction of a digital library of vocal resources.

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“…In the first part of the analysis, the proposed SSARM and the benchmarked GWORM, WOARM and SERA approaches are investigated based on different service arrival rates [20][21][22][23][24]. The simulation envoironment comprises of gateways and resources that colud be possibly allocated to suitable gateways.…”

Section: Resultsmentioning

confidence: 99%

Sparrow Search Algorithm-based Resource Management in Internet of Things (IoT)

Karthick¹,

Gomathi²

2018

EAI Endorsed Transactions on Energy Web

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INTRODUCTION:The principle of Internet of Things (IoT) completely concentrates on integrating heterogeneous entities for establishing seamless cooperation among virtual and physical entities. IoT has facilitated a paradigm shift of the Internet from collaborating networks to physical world interaction. The IoT devices are potent in sensing, processing, communicating and data storing that are derived from the physical world. The majority of the IoT applications are considered to be pervasive in characteristics and face huge challenges due to unattended IoT devices and constrained resources. OBJECTIVES: The proposed Sparrow Search Algorithm-based Resource Management (SSARM) targets on the potential assignment of resources to gateways in an IoT environment with better establishment in maintaining the tradeoff between intensification and diversification. It concentrates on the reduction of total data transmission cost in the IoT environment. METHODS: In this paper, Sparrow Search Algorithm-based Resource Management (SSARM) is proposed based on the inspired by the foraging, group wisdom and anti-predation characteristics of sparrow for potential assignment of multiple resources to gateways in IoT. This SSARM balances the degree of exploitation and exploration in the optimization search space to an acceptable level. RESULTS: The simulation results of this proposed SSARM confirmed better throughput of 23.82%, reduced delay of 18.21% and minimized energy consumption of 20.28% when compared to the existing schemes. CONCLUSION: This SSARM offers better accuracy in stability, convergence rate, precision in searching and preventing the value of local point of optimality.

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The Application of Fog Computing and Internet of Things Technology in Music Resource Management Model

Cited by 9 publications

References 27 publications

Design of the Music Intelligent Management System Based on a Deep CNN

Design of the Music Intelligent Management System Based on a Deep CNN

Construction of a digital library of vocal music resources based on second-order data decomposition algorithm

Sparrow Search Algorithm-based Resource Management in Internet of Things (IoT)

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