文献类型：期刊文献

英文题名：Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network

作者：Mi, Dan[1];Qin, Lu[2]

第一作者：米丹

通讯作者：Qin, L[1]

机构：[1]Xinxiang Univ, Dept Mus, Xinxiang 453003, Henan, Peoples R China;[2]Xinxiang Univ, Dept Sports, Xinxiang 453003, Henan, Peoples R China

第一机构：新乡学院音乐学院

通讯机构：[1]corresponding author), Xinxiang Univ, Dept Sports, Xinxiang 453003, Henan, Peoples R China.|[11071]新乡学院;

年份：2022

卷号：2022

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：;EI(收录号：20224413024761);Scopus(收录号：2-s2.0-85140347662);WOS:【SCI-EXPANDED(收录号:WOS:000876419800010)】；

语种：英文

外文关键词：Audio acoustics - Deep learning - Information retrieval systems - Inverse problems - Neural networks - Spectrographs - Statistical tests - Time domain analysis

摘要：National music is a treasure of Chinese traditional culture. It contains the cultural characteristics of various regions and reflects the core value of Chinese traditional culture. Classification technology classifies a large number of unorganized drama documents, which are not labeled, and to some extent, it helps folk music better enter the lives of ordinary people. Simulate folk music of different spectrum and record corresponding music audio under laboratory conditions Through Fourier transform and other methods, music audio is converted into spectrogram, and a total of 2608 two-dimensional spectrogram images are obtained as datasets. The sonogram dataset is imported into the deep convolution neural network GoogLeNet for music type recognition, and the test accuracy is 99.6%. In addition, the parallel GoogLeNet technology based on inverse autoregressive flow is used. The unique improvement is that acoustic features can be quickly converted into corresponding speech time-domain waveforms, reaching the real-time level, improving the efficiency of model training and loading, and outputting speech with higher naturalness. In order to further prove the reliability of the experimental results, the spectrogram datasets are imported into Resnet18 and Shufflenet for training, and the test accuracy of 99.2% is obtained. The results show that this method can effectively classify and recognize music. The experimental results show that this scheme can achieve more accurate classification. The research realizes the recognition of national music through deep learning spectrogram classification for the first time, which is an intelligent and fast new method of classification and recognition.