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Designing an online vocal learning based on ZigBee-enabled wireless platform

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Abstract

Vocal learning has found its proliferation in recent years due to the advancement in wireless networking. Vocal art encompasses profound cultural values and aesthetic preferences, making wireless platforms, e.g. Wi-Fi and ZigBee, essential for the enhancement of online vocal learning. ZigBee, as a wireless platform, has more recently been used quite frequently for online vocal learning and music. This paper aims to design an efficient wireless-enabled platform by using the lightweight features of ZigBee to support online vocal learning and expressions. The platform enables data transmission, storage, analysis, and playback of vocal learning sounds through the ZigBee network. It is built on a cloud platform, utilizing Docker virtualization technology to deploy a Hadoop distributed cluster, effectively simulating a distributed environment. The platform incorporates a model for recognizing musical sound and noise signals, empowering the online learning control platform to detect various characteristics of vocal music tones. Vocal music learning is enhanced by extracting endpoint fusion spectrum features and employing a tone adjustment and dynamic detection model. The proposed method improves anti-interference capability, online learning control, vocal tone recognition, and emotional expression accuracy. Simulation results demonstrate its effectiveness in vocal music, online learning control, and tone recognition, leading to more precise vocal music pronunciation and intonation registration.

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The data used to support the findings of this study are available from the corresponding author upon request.

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  1. Nanjing Normal University Taizhou College, Taizhou City, Jiangsu, China

    Mingxia Wan

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  1. Mingxia Wan
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Correspondence to Mingxia Wan.

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Wan, M. Designing an online vocal learning based on ZigBee-enabled wireless platform. Wireless Netw 30, 179–192 (2024). https://doi.org/10.1007/s11276-023-03443-0

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