使用机器学习的网络远程音乐协作

Nishtha Nayar, Divya Lohani
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引用次数: 0

摘要

使用视频和音频等媒介进行交流对许多职业来说都是必不可少的。在本文中,使用物联网和机器学习领域提供的工具来研究与实时音频传输的交互。传输层协议- TCP和UDP检查音频传输质量。此外,研究了不同的递归神经网络(RNN)模型在传输过程中丢失数据包的情况下预测音乐的效率。这些预测填补了空白,并有助于防止任何延迟在实时表演/堵塞,其中丢失的音符可能是一个障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Networked Remote Music Collaboration using Machine Learning
Communication using mediums like video and audio are essential for a lot of professions. In this paper, interaction with real time audio transmission is looked upon using the tools provided by the domains of IoT and Machine Learning. The transport layer protocols - TCP and UDP are examined for audio transmission quality. Further, different Recurrent Neural Networks (RNN) models are examined for their efficiency in predicting music in case of loss of packets during transmission. These predictions fill in the gaps and help prevent any lag in a real time performance/jam where the loss of musical notes can be a hindrance.
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