[1]LIU Wanjun,MENG Renjie,QU Haicheng,et al.Music genre recognition research based on enhanced AlexNet[J].CAAI Transactions on Intelligent Systems,2020,15(4):750-757.[doi:10.11992/tis.201909032]
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Music genre recognition research based on enhanced AlexNet

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 750-757 Column: 学术论文—知识工程 Public date: 2020-07-05
Title:
Music genre recognition research based on enhanced AlexNet
Author(s):
LIU Wanjun;  MENG Renjie;  QU Haicheng;  LIU Lamei
College of Software, Liaoning Technical University, Huludao 125105, China
Keywords:
music genres recognition; deep convolutional neural network; machine learning; deep learning; AlexNet; audio feature extraction; audio feature extraction
CLC:
TP181
DOI:
10.11992/tis.201909032
Abstract:
To solve the problem that machine learning model has weak ability to identify music genre features, a music genre recognition model based on deep convolutional neural network (DCNN-MGR) is proposed in this paper. At first, the model extracts audio information through Fast Fourier Transformation, generating spectrums that can be input to the DCNN and slicing the generated spectrums. Then AlexNet is enhanced by fusion of Leaky ReLU function, Tanh function and Softplus classifier. The generated spectrum slices are input into the enhanced AlexNet for multi-batch training and verification. Music features are extracted and learned, and a network model that can effectively distinguish music features is obtained. At last, the output model is applied to music genre recognition and test. The experimental results show that the enhanced AlexNet is superior to AlexNet and other commonly used DCNN in terms of accuracy of music feature recognition and network convergence effect. The DCNN-MGR model is 4%~20% higher than other machine learning models in music genre recognition accuracy.
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