Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm

Lu, Lifei; Xu, Lida; Xu, Bin; Li, Guoqiang; Cai, Hongming

doi:10.1109/tcss.2018.2871694

Cited by 36 publications

(20 citation statements)

References 25 publications

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“…Ghobakhloo & Ching, 2019;Kushwaha et al, 2020;Malhotra & Rishi, 2019;Mazurek & Małagocka, 2019;Oyelude, 2017;Pradhan & Chawla, 2020;Simou, Tiligadis, & Alexiou, 2013;Xu, 1999). Other studies explore the theoretical foundation for AI (Chi-Hsien & Nagasawa, 2019;Haenlein, Kaplan, Tan, & Zhang, 2019;Kurade & Latpate, 2020;Murphy, 2018;Tung, 2019;Xu & Li, 2000;Zou, 2014) and applications of AI (Duan & Xu, 2012;Kouziokas, 2017;Lu, 2019;Lu, Xu, Xu, Li, & Cai, 2018;Qiu, 2018;Sanchez, Exposito, & Aguilar, 2020;Xu, Liang, & Gao, 2001). However, few studies investigate AI adoption, particularly at the organizational level.…”

Section: It Adoption and Ai Adoptionmentioning

confidence: 99%

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Chen

2020

Journal of Management Analytics

107

104

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As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The study provides support for firms' decision-making and resource allocation regarding AI adoption.

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Section: It Adoption and Ai Adoptionmentioning

confidence: 99%

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Chen

2020

Journal of Management Analytics

107

104

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“…Music feature recognition is based on the art of speech recognition development, where the audio signal is used to obtain the musical content and the musical features such as musical patterns and emotions [1].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

Kai¹

2021

Complexity

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Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things (IoT) technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion.

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“…The main advantage of fog computing is the reduction of the end to end latency of an application. This study complements the existing architecture [15][16][17][18][19][20][21][22][23][24] which is based on SOA. The work creates a system which consists of three layers such as user-end, fog end and cloud end as shown in Figure 1.…”

Section: The Proposed Solutionmentioning

confidence: 90%

A Microservice-Based System for Industrial Internet of Things in Fog-Cloud Assisted Network

Khoso

Lakhan

Ahmed

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Nowadays, the usage of the Industrial Internet of Things (IIoT) in practical applications has increased. The primary utilization is a fog cloud network, which offers different services, such as network and remote edges, at different places. Existing studies implemented the Service-Oriented Architecture (SOA) based on the fog-cloud network to run IIoT applications, such as e-healthcare, e-agriculture, renewable energy, etc. However, due to the applications' monolithic property, issues like failures, security, and cost factors occur, e.g. the failure of one service in SOA affects monolithic applications' performance in the system. With this motivation, this study suggests a microservice-based system to deal with the cost, security, and failure risks of IIoT applications in the fog-cloud system. The study improves the existing SOA systems for e-healthcare, e-agriculture, and renewable energy and minimizes the applications' overall cost. The performance evaluation shows that the devised systems outperform the existing SOA system in terms of failure, cost, and the deadline for all applications.

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Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm

Cited by 36 publications

References 25 publications

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Explore success factors that impact artificial intelligence adoption on telecom industry in China

Optimization of Music Feature Recognition System for Internet of Things Environment Based on Dynamic Time Regularization Algorithm

A Microservice-Based System for Industrial Internet of Things in Fog-Cloud Assisted Network

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