A Hybrid CNN and RNN Variant Model for Music Classification

Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been significantly considered. Additionally, addressing the flaws in the particular neural network classification model, this paper proposes a hybrid architecture of CNN and variants of RNN such as long short-term memory (LSTM), Bi-LSTM, gated recurrent unit (GRU), and Bi-GRU. We also compared the performance based on Mel-spectrogram and Mel-frequency cepstral coefficient (MFCC) features. Empirically, the proposed hybrid architecture of CNN and Bi-GRU using Mel-spectrogram achieved the best accuracy at 89.30%, whereas the hybridization of CNN and LSTM using MFCC achieved the best accuracy at 76.40%.

Automated summary

Music genre classification plays a significant role in organizing large collections of music for information retrieval. Traditional methods using handcrafted features struggle to accurately determine the original music characteristics. This paper proposes a hybrid architecture of CNN and RNN variants, such as LSTM, Bi-LSTM, GRU, and Bi-GRU, to address the limitations of existing neural network classification models. Experimental results show that the CNN-Bi-GRU architecture using Mel-spectrogram achieves the highest accuracy of 89.30%, while the CNN-LSTM hybridization using MFCC achieves the highest accuracy of 76.40%.