A Monte Carlo Search-Based Triplet Sampling Method for Learning Disentangled Representation of Impulsive Noise on Steering Gear

Abstract

The classification task of impact noise on vehicle steering system mainly addresses the issue of modeling the transient and impulsive nature. Though various deep learning models including triplet network have been developed, the existing triplet network based on Euclidean distance metric is limited due to the simplicity of distance measure against reverberation generated from the narrow interior space and the low frequency difference generated from the interior finishes. In this paper, we propose a method to overcome the above two major hurdles by modify a sampling algorithm of triplet pairs based on structural similarity index instead of naive Euclidean distance within Monte Carlo based sampling strategy. We verify the proposed modified triplet loss through cross-validation that the proposed sampling method has more than 3% of accuracy improvement with computational cost reduction against the existing triplet networks. The detailed analysis shows that the proposed method can potentially compensate for the disjoint issues between the learning and validation vehicle types.