The intelligent classification method of aircraft cockpit sound based on deep learning

The critical background sounds in the cockpit provide important evidence for flight monitoring evaluations and accident investigations. Regarding the high complexity and large data requirements of cockpit voice recorder(CVR) audio recognition, the issue of identifying low-frequency transient background sounds is particularly challenging, along with the interference caused by engine noise, an intelligent classification method of CVR background sounds based on deep learning is paper proposed. A dataset of 10 types of CVR background sounds was established, with acoustic features extracted by using three spectrogram methods, and a time-delay neural network model was built. Context-aware masking modules were used to reduce the impact of noise on operational sounds, while the front-end convolution module captured low-frequency transient signals. This study optimized a hybrid convolutional and time-delay neural network model, TDNN-CF. The improved model achieved a classification accuracy of 98.90%, representing increases of 13.04 and 2.99 percentage points comparing with the traditional CNN and TDNN models, respectively. Additionally, comparing with the classic machine learning algorithms like decision trees, random forests, and K-nearest neighbors(KNN), accuracy improved by 18.07, 15.62, and 14.55 percentage points, respectively. Experimental results show that the present method efficiently classifies CVR audio.