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