Scholarlink

<p>The rapid developments in mobile network technologies and audio processing systems have allowed the creation of high-quality audio applications, such as collaborative Western orchestra compositions. However, the effective transmission and management of mobile network traffic for such applications are dangerous to confirm continuous performance. This paper discovers the use of advanced mobile network traffic classification techniques, mainly the integration of hybrid deep learning models, to optimize network performance for real-time audio data streaming. Using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the study proves how network traffic can be classified and managed to recover Quality of Service (QoS), addressing challenges like latency, bandwidth limitations, and network congestion. The results show that the proposed methodology attains outstanding performance, with an accuracy of 98.8%, precision of 99.2%, recall of 98.3%, and an F1 score of 98.8%. These results signify that the model is highly effective in enhancing the overall workflow for remote orchestral composition by ensuring smooth, high-quality audio transmission.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application of Mobile Network Traffic Classification in Audio Processing Design for Western Orchestra Creation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

查看全文
  • Jingliang Wang

摘要

The rapid developments in mobile network technologies and audio processing systems have allowed the creation of high-quality audio applications, such as collaborative Western orchestra compositions. However, the effective transmission and management of mobile network traffic for such applications are dangerous to confirm continuous performance. This paper discovers the use of advanced mobile network traffic classification techniques, mainly the integration of hybrid deep learning models, to optimize network performance for real-time audio data streaming. Using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the study proves how network traffic can be classified and managed to recover Quality of Service (QoS), addressing challenges like latency, bandwidth limitations, and network congestion. The results show that the proposed methodology attains outstanding performance, with an accuracy of 98.8%, precision of 99.2%, recall of 98.3%, and an F1 score of 98.8%. These results signify that the model is highly effective in enhancing the overall workflow for remote orchestral composition by ensuring smooth, high-quality audio transmission.