音频语义通信的联邦学习

Haonan Tong, Zhaohui Yang, Sihua Wang, Ye Hu, Omid Semiari, W. Saad, Changchuan Yin
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引用次数: 25

摘要

本文研究了无线网络中音频语义通信的问题。在考虑的模型中,无线边缘设备使用语义通信技术将大型音频数据传输到服务器。该技术允许设备仅传输捕获音频信号上下文特征的音频语义信息。为了从音频信号中提取语义信息,提出了一种基于波向量(wav2vec)结构的自编码器,该编码器由卷积神经网络(cnn)组成。所提出的自动编码器能够以少量数据实现高精度音频传输。为了进一步提高语义信息提取的准确性,在多台设备和一台服务器上实现了联邦学习(FL)。仿真结果表明,与传统编码方案相比,该算法能有效收敛,并能将音频传输的均方误差(MSE)降低近100倍。
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Federated Learning for Audio Semantic Communication
In this paper, the problem of audio semantic communication over wireless networks is investigated. In the considered model, wireless edge devices transmit large-sized audio data to a server using semantic communication techniques. The techniques allow devices to only transmit audio semantic information that captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder is proposed, which consists of convolutional neural networks (CNNs). The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean squared error (MSE) of audio transmission by nearly 100 times, compared to a traditional coding scheme.
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