# An Experimental Performance Assessment of Temporal Convolutional Networks for Microphone Virtualization in a Car Cabin

## TL;DR

This paper evaluates how well Temporal Convolutional Networks can estimate sound in a car cabin using virtual microphones under different conditions.

## Contribution

The study introduces a TCN-based approach for robust microphone virtualization in cars, validated in realistic and varied acoustic scenarios.

## Key findings

- The TCN adapts well to different cabin conditions and maintains good performance.
- The study identifies optimal NN parameters balancing accuracy and computational efficiency.

## Abstract

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.