This paper presents a method for generating album cover art by including side information regarding the music content. In this preliminary work, using state of the art Generative Adversarial Networks (GAN), album cover arts are generated given a genre tag. In order to have a sufficient dataset containing both the album cover and genre, the Spotify API was used to create a dataset of 50,000 images separated into 5 genres. The main network was pre-trained using the One Million Audio Cover Images for Research (OMACIR) dataset and then trained on the Spotify dataset. This is shown to be successful as the images generated have distinct characteristics for each genre and minimal repeated textures. The network can also distinguish which genre a generated image comes from with an accuracy of 35%.
|Number of pages||7|
|Publication status||Published - 6 Oct 2017|
|Event||10th International Workshop on Machine Learning and Music - Barcelona, Spain|
Duration: 6 Oct 2017 → …
|Conference||10th International Workshop on Machine Learning and Music|
|Period||6/10/17 → …|