基于改进反向传播算法的声音识别及健康检测技术

Voice Recognition and Health Detection Technology Based on Improved Backpropagation Algorithm

随着计算机技术的发展,声音识别与健康检测成为现代医学诊断的重要手段之一;通过对新生婴儿声音的分析,可以早期发现和诊断多种健康问题;研究提出一种基于改进反向传播神经网络的声音识别模型,通过声音实现对新生儿的健康状况分析;该模型通过小波变换对声音数据进行预处理,随后结合粒子群优化算法和反向传播神经网络设计检测模型;通过引入粒子群优化算法对反向传播算法进行改进,提高了模型的局部搜索能力和收敛速度;实验结果表明,在数据集为1000时,小波去噪模型的信噪比为0.97,结构信息损失率为0.18,交并比为0.96;针对不同类型的声音,改进反向传播神经网络模型识别的准确率分别为0.87、0.83、0.97、0.88,均方根误差值为0.09、0.07、0.05、0.07;结果表明,所提出的声音识别与健康检测模型能够有效提高声音数据的识别精度和检测效率,有助于新生儿健康状态的评估。

With the development of computer technology,voice recognition and health monitoring have become important means of modern medical diagnosis.By analyzing the sounds of newborn babies,various health problems can be detected and diagnosed early.A voice recognition model based on improved back propagation neural network is proposed,the health status of newborns is analyzed through sound.This model is used to preprocess sound data through wavelet transform,and then combined with particle swarm optimization algorithm and backpropagation neural network to design the detection model.The back propagation algorithm is improved by introducing particle swarm optimization algorithm,enhancing the local search ability and convergence speed of the model.Eeperimental results show that with a dataset of 1000,the signal-to-noise ratio of the wavelet denoising model is 0.97,the structural information loss rate is 0.18,and the intersection to union ratio is 0.96.The recognition accuracy of the improved back propagation neural network model is 0.87,0.83,0.97,and 0.88 for different types of sounds,with the root mean square errors of 0.09,0.07,0.05,and 0.07,respectively.The results indicate that the proposed voice recognition and health detection model can effectively improve the recognition accuracy and detection efficiency of voice data,which is helpful for evaluating the health status of newborns.