用神经编码器在基带FM电台上传输高保真语音
RADE for Land Mobile Radio: A Neural Codec for Transmission of Speech over Baseband FM Radio Channels
- 用自编码器将语音转为数字脉冲信号发送
- 在衰落信道下语音质量优于传统模拟FM
- 可在普通UHF对讲机上实时运行，适合应急通信
1990年代，陆地移动无线电（LMR）系统从模拟调频（FM）演进为标准化数字系统。如今数字与模拟FM系统共存，语音质量相当。许多数字对讲机保留了原有模拟FM调制解调器，但输入由多电平数字脉冲流替代模拟语音信号，这种架构称为基带FM（BBFM）。本文提出一种基于神经网络的现代机器学习方法，通过自编码器实现8 kHz带宽语音在BBFM信道上的传输。实验表明，在存在衰落的模拟LMR信道中，该方案语音质量优于传统模拟FM。同时展示了系统在商用UHF对讲机上的实际运行效果。
原文摘要 · Abstract (English)
In the 1990s Land Mobile Radio (LMR) systems evolved from analog frequency modulation (FM) to standardised digital systems. Both digital and analog FM systems now co-exist in various services and exhibit similar speech quality. The architecture of many digital radios retains the analog FM modulator and demodulator from legacy analog radios, but driven by a multi-level digital pulse train rather than an analog voice signal. We denote this architecture baseband FM (BBFM). In this paper we describe a modern machine learning approach that uses an autoencoder to send high quality, 8 kHz bandwidth speech over the BBFM channel. The speech quality is shown to be superior to analog FM over simulated LMR channels in the presence of fading, and a demonstration of the system running over commodity UHF radios is presented.
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