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Deep Learning for Audio Signal Source Positioning Using Microphone Array

机译:使用麦克风阵列进行音频信号源定位的深度学习

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This paper deals with a deep learning of audio signal source positioning. When there is no cooperation between signal source and receiving microphones, it is best way to employ the time difference of arrival (TDOA) task for source positioning. Therefore, this paper considers positioning using a microphone array and TDOA method. Alongside with analytical methods, such as triangulation, this study concentrates on statistical signal processing and deep learning by neural network. Considering the location finding applications the idea of using a neural network algorithm is quite new and novel, but its performance was questionable before presented study. This study shows that neural network type deep learning algorithm performs better over analytical techniques and provides much faster location estimate, as it is presented in this study.
机译:本文涉及音频信号源定位的深度学习。当信号源和接收麦克风之间没有协作时,最好采用到达时间差(TDOA)任务进行信号源定位。因此,本文考虑使用麦克风阵列和TDOA方法进行定位。除了三角剖分法之类的分析方法外,本研究还专注于统计信号处理和通过神经网络进行的深度学习。考虑到位置查找的应用,使用神经网络算法的想法是相当新颖的,但是在提出研究之前,其性能值得怀疑。这项研究表明,神经网络型深度学习算法的性能优于分析技术,并且提供了更快的位置估计,正如本研究中所述。

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