Abstract: Aiming at the challenge of real-time implementation and high classification accuracy for hearing aids, a background noise classification algorithm based on the LightGBM ensemble learning is proposed to reduce the computational time in the process. A newly proposed band spectral correlation feature concatenated with the band spectral entropy feature is also presented. This new acoustic feature is formed to improve the noise classification accuracy. Binaural differential signal is used to extract the band spectral features for reducing memory occupation and offline training workload, so as to improve the computational efficiency. Six commonly encountered noise environments of quiet indoors, traffic, wind turbulence, music, cocktail party and vehicle noise from hearing aid research dataset for acoustic environment recognition are considered. The experimental results show that our proposed algorithm significantly improves the performance of the background noise classification in real-time implementation and the accuracy compared with the algorithms based on the random forest model and band features.