Statistical Classification of Vehicle Interior Sound Through Upsampling-Based Augmentation and Correction Using 1D CNN and LSTM

Abstract

To quantitatively classify the results provided by engineers, we built a sound quality (SQ) classification model using neural networks. The data used in this study were recorded wav files obtained from various vehicle specifications while driving under wide open throttle conditions. However, the data lengths were not constant. The upsampling and interpolation scheme (USIS) was used to achieve constant data lengths. After the USIS was applied to the data, dynamic time warping was used to verify that there was no change in the data characteristics. The verified dataset was transformed into Mel-spectrogram to confirm the characteristics, and dimensionality reduction was applied by using a high-pass filter. Clarifying the differences between clusters improves the model performance. The classification models of 1D convolutional neural network and long short-term memory exhibited training accuracies of about 94.9% (64 or 65 out of 68 classified) and test accuracies of about 87.5% (7 out of 8 classified). For additional undefined label classification, the quantitative evaluation and statistical classification of undefined sound quality labels are successfully identified in the present study. Both neural networks produced effective results that can be used by sound design engineers to quantitatively examine the SQ of vehicle interior noise.