Opinto Alessandro, Martalò Marco, Straccia Riccardo, Raheli Riccardo
Keysight Technologies Italy S.r.l., 20127 Milan, Italy.
Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy.
Sensors (Basel). 2024 Aug 10;24(16):5163. doi: 10.3390/s24165163.
In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions-a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented.
本文展示了在实际汽车场景中麦克风虚拟化的实验结果。设计了一个时间卷积网络（TCN），以便根据不同位置监测麦克风信号的信息来估计驾驶员耳部位置的声学信号——这是一种被称为虚拟麦克风的技术。在一款流行的B级汽车上搭建了一个实验装置，以获取汽车在不同速度下于平坦沥青路面行驶时车厢内的声场。为了测试TCN的潜力，在有或没有前排乘客的两种不同场景下记录了麦克风信号。我们的实验结果表明，当在两种场景下进行训练时，所采用的TCN能够稳健地适应不同条件并保证良好的平均性能。此外，还对神经网络（NN）的参数进行了研究，这些参数在保持低计算复杂度的同时能确保虚拟麦克风信号估计的足够准确性。
