基于子带谱特征的助听器背景噪声场景分类算法

针对助听器应用中背景噪声场景分类算法需同时具备低延时性和高分类准确率的问题，提出一种基于LightGBM集成学习模型的助听器场景分类算法以减少分类过程的计算时间，给出一种新的子带谱相关性特征并联合子带谱熵特征构成分类特征来提高助听器场景分类的准确率，使用双耳差分信号提取子带谱特征减少计算过程中的内存占用率以及模型离线训练工作量，提高计算效率。对双耳助听器声学环境识别数据集中的安静室内、交通环境、风噪声、音乐、鸡尾酒会、汽车噪声6种场景下的背景声音进行测试，实验结果表明，相对于基于随机森林模型和子带信号周期性特征、子带信号熵特征的场景分类算法，该算法在实时性和分类准确率方面的性能均有显著改善。

Background Noise Classification Algorithm for Hearing Aids Based on the Band Spectral Features

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.