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In recent years, deep learning has emerged in the audio field with many excellent models and beats non-depth methods in the quality of generated audio. This paper implements a symbol-based end-to-end music generation model. This model generates piano music corresponding to the pitch of the musical score using a two-dimensional “Piano-roll” liked structure as input. The experiments show the generated music obtains good performance and achieves a result similar to the original song in pitch, melody, and timbre. Compared with other generation methods, the input of our model is simple, easy to obtain, and can generate music through an end-to-end method.

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Network embedding has been proven to be helpful for solving real-world problems. Moreover, real-world networks are often heterogeneous information networks(HINs). In this paper, we propose a new adversarial framework for heterogeneous network embedding, namely AGNE-HIN. AGNE-HIN can learn latent code distribution in the network through a generative adversarial way. What’s more, to reduce the global smoothness of the embedded vector caused by GAN, we apply perturbation to the input to form adversarial data. Experimental results verify our design and demonstrate the effectiveness of the proposed method in node clustering, link prediction and similarity ranking tasks.