A study on classifying BSR noise in automotive door trim using a deep learning method

In this paper, we demonstrated a deep learning model that can automatically classifies BSR(Buzz, Squeak, Rattle) noise in automotive door trim using convolutional neural networks. In the preprocessing process, sound features of door trim noises are extracted by using STFT(Short-time Fourier Transform) algorithm to obtain spectrogram images for each noises we obtained from experiment. To classify various noises of door trim, those image data are labeled with its root cause, then it is trained with deep convolutional neural network. Furthermore, t-SNE (t-Stochastic Neighbor Embedding) algorithm was used to visualize the effect of deep convolutional neural network we demonstrated. The classifier of the demonstrated method achieved with the accuracy of 96%.