Deep Learning-based Visualization of Music Mood
Hanqin Wang, Alexei Sourin · 2023
Music has been visualized in different forms. Majority of the existing methods of music visualization utilized only a select few parameters of the music, of which pitch and frequency were the most visualized. Also, current musk visualizers usually produce animated visual backgrounds for the music being played. Visualization of music as static images was only addressed by some people who claimed to have a neurological condition called synesthesia or chromesthesia. In this paper, we use artificial intelligence to simulate such music visualization. Specifically, we consider how the mood of music can be visualized in static images using deep learning techniques. We consider two approaches. First, we use the deep learning network to generate abstract paintings based on the music sentiments obtained from the Spotify library. In the second approach, two different deep learning networks are used to both classify the musk and to generate the respective landscape images representing its mood. The results are analyzed and compared as well as subjected to user tests.