  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.
