评估和减轻声学环境对机器对机器信令的影响
Estimating & Mitigating the Impact of Acoustic Environments on Machine-to-Machine Signalling
摘要：
在各种物联网和电信应用中传输声音数据的技术的进步导致了机器对机器空中声学信令的概念。在解码时，混响可能对这种机器对机器信号具有不利影响。人们已经研究了各种方法来对抗语音和音频信号中的混响效应，但尚不清楚它们对其他声音类型的推广效果如何。我们着眼于扩展这些模型，以促进机器对机器的声学信号。本研究探讨混响消除技术，以筛选出一种单通道混响抑制方法。为了应用所选择的去混响方法，提出了一种新的估计控制混响的声学参数的方法。最终算法的性能进行评估的质量指标，以及一个真正的机器到机器解码器的性能。我们证明了一个戏剧性的降低错误率为可听和超声波信号。
英文摘要：
The advance of technology for transmitting Data-over-Sound in various IoT and telecommunication applications has led to the concept of machine-to-machine over-the-air acoustic signalling. Reverberation can have a detrimental effect on such machine-to-machine signals while decoding. Various methods have been studied to combat the effects of reverberation in speech and audio signals, but it is not clear how well they generalise to other sound types. We look at extending these models to facilitate machine-to-machine acoustic signalling. This research investigates dereverberation techniques to shortlist a single-channel reverberation suppression method through a pilot test. In order to apply the chosen dereverberation method a novel method of estimating acoustic parameters governing reverberation is proposed. The performance of the final algorithm is evaluated on quality metrics as well as the performance of a real machine-to-machine decoder. We demonstrate a dramatic reduction in error rate for both audible and ultrasonic signals.
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