The main task of music recognition is to acquire relevant information of music content through processing and feature extraction of audio signals, and then used for comparison, classification, and automatic recording. The cognitive neural network based on T-S model is used to train the network weights with improved genetic algorithm in the paper. The strategy of membership function parameter adjustment is combined with the combination of momentum method and learning rate adaptive adjustment. The new proposed algorithm can be used in the music recognition algorithm by adding a compensation factor related to the input dimension on the membership degree, and the experimental result of the rule disaster caused by the excessive input dimension shows that the new proposed method can be applied to the music recognition system. At the same time, it shows that the accuracy rate of the recognition network is more accurate than that of the other algorithms, and its robustness is better.

中文翻译：

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基于TS认知神经网络的音乐识别算法。

音乐识别的主要任务是通过音频信号的处理和特征提取来获取音乐内容的相关信息，然后用于比较，分类和自动录制。本文基于改进的遗传算法，采用基于TS模型的认知神经网络训练网络权重。隶属函数参数调整策略与动量法和学习率自适应调整相结合。通过在隶属度上增加与输入维有关的补偿因子，可以将该新算法用于音乐识别算法，输入维数过大引起的规则灾难的实验结果表明，该方法可以适用。音乐识别系统。与此同时，