Abstract
Nowadays, music genre classification is becoming an interesting area and attracting lots of research attention. Multi-feature model is acknowledged as a desirable technology to realize the classification. However, the major branches of multi-feature models used in most existed works are relatively independent and not interactive, which will result in insufficient learning features for music genre classification. In view of this, we exploit the impact of learning feature interaction among different branches and layers on the final classification results in a multi-feature model. Then, a middle-level learning feature interaction method based on deep learning is proposed correspondingly. Our experimental results show that the designed method can significantly improve the accuracy of music genre classification. The best classification accuracy on the GTZAN dataset can reach 93.65%, which is superior to most current methods.
Highlights
With the rise of music streaming media services, tens of thousands of digital songs have been uploaded to the Internet
Hafemann et al [21] confirmed that a Convolution Neural Network (CNN) can mine rich texture information from the spectrum and improve the performance of music classification, because CNN is very sensitive to the texture information of the image
This paper proposes a middle-level learning feature interaction method using deep learning
Summary
With the rise of music streaming media services, tens of thousands of digital songs have been uploaded to the Internet. Hafemann et al [21] confirmed that a Convolution Neural Network (CNN) can mine rich texture information from the spectrum and improve the performance of music classification, because CNN is very sensitive to the texture information of the image For this reason, music genre classification based on visual features has achieved remarkable results in recent years. Most of the existing methods are mainly modified based on the CNN model of image recognition These methods take audio spectrogram, Mel-frequency spectrogram and other visual features as the input. Our research verifies that in the multi-feature model using MLFI, the learning features close to the input and output have a better impact on improving the classification accuracy, which is an important and core contribution of this research. The model extracts significant differences from the Mel-frequency spectrogram of each genre and conducts audio classification tasks based on autonomous learning. This set of audio features (as shown in Table 1) provides the best results for our experiments
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