Effect of audio pre-processing technique for neural network on lung sound classification

In this paper, we intended to study the effect of two audio pre-processing methods to multilayer feedforward network performance on lung sound classification problem. The first one is Mel Frequency Cepstral Coefficients (MFCC) and the second is the MFCC supplement with Linear Discriminant Analysis (LDA). Datasets applied in this study came from Kaggle Respiratory Sound Database, which is the largest resource for machine learning. As a result, MFCC supplement with LDA has both good performance and significant improvement than conventional MFCC. (paper)