Music Genre Classification with Paralleling Recurrent Convolutional Neural Network
This work addresses music genre classification, an incremental improvement for audio processing applications.
The authors tackled music genre classification by proposing a hybrid architecture combining CNN and Bi-RNN blocks to extract spatial and temporal features, which improved classification performance as proven by experiments.
Deep learning has been demonstrated its effectiveness and efficiency in music genre classification. However, the existing achievements still have several shortcomings which impair the performance of this classification task. In this paper, we propose a hybrid architecture which consists of the paralleling CNN and Bi-RNN blocks. They focus on spatial features and temporal frame orders extraction respectively. Then the two outputs are fused into one powerful representation of musical signals and fed into softmax function for classification. The paralleling network guarantees the extracting features robust enough to represent music. Moreover, the experiments prove our proposed architecture improve the music genre classification performance and the additional Bi-RNN block is a supplement for CNNs.