This invention discloses a
system and method for labeling automotive abnormal
noise data, relating to the field of automotive NVH detection technology. The method includes: receiving raw
signal data of automotive abnormal
noise; performing time-frequency transformation on the raw
signal to generate a time-frequency diagram; identifying key frequency ranges characterizing the abnormal
noise features; then performing
directional filtering to enhance the abnormal noise
signal; identifying candidate time ranges for abnormal noise through an automatic detection
algorithm; and outputting the final abnormal noise
time range labels and key frequency ranges in a structured format after
verification and correction. The
system includes a data receiving module, a
signal processing module, a display module, an intelligent labeling module, an audio playback module, a labeling output module, and an interaction module, achieving triple-assisted labeling through auditory, visual, and intelligent pre-selection methods. This solves the problems of low efficiency, low accuracy, and poor consistency in traditional manual listening methods, providing high-quality
labeled data for
deep learning classification of automotive abnormal noises.