  In this paper we propose a deep learning method for performing
attributed-based music-to-image translation. The proposed method is applied for
synthesizing visual stories according to the sentiment expressed by songs. The
generated images aim to induce the same feelings to the viewers, as the
original song does, reinforcing the primary aim of music, i.e., communicating
feelings. The process of music-to-image translation poses unique challenges,
mainly due to the unstable mapping between the different modalities involved in
this process. In this paper, we employ a trainable cross-modal translation
method to overcome this limitation, leading to the first, to the best of our
knowledge, deep learning method for generating sentiment-aware visual stories.
Various aspects of the proposed method are extensively evaluated and discussed
using different songs.
