Music Generation Using Bidirectional Recurrent Network

With the development of deep learning, neural networks are increasingly used in various art fields such as music, literature and pictures, and even comparable to humans. This paper proposes a music generation model based on bidirectional recurrent neural network, which can effectively explore the complex relationship between notes and obtain the conditional probability from time and pitch dimensions. The existing system usually ignored the information in the negative time direction, however which is non-trivial in the music prediction task, so we propose a bidirectional LSTM model to generate the note sequence. Experiments with classical piano datasets have demonstrated that we achieve high performance in music generation tasks compared to the existing unidirectional biaxial LSTM method.