A deep learning system that can identify the emotion and intensity of a song by using a range of data, rather than focusing on a single element, such as the instrumentation, has been developed by researchers at Deezer. The AI was trained using a combination of audio signals, linguistic data and aggregation of Deezer tags describing tracks. The resulting 18,644-song database was then used to enable the system to gauge the mood of different tracks. While the service is not yet ready for widespread use on Deezer or other platforms, it could lead to smarter song playlists in the future.
A deep learning system that can identify the emotion and intensity of a song by using a range of data, rather than focusing on a single element, such as the instrumentation, has been developed by researchers at Deezer. The AI was trained using a combination of audio signals, linguistic data and aggregation of Last.fm tags describing tracks. The resulting 18,644-song database was then used to enable the system to gauge the mood of different tracks. While the service is not yet ready for widespread use on Deezer or other platforms, it could lead to smarter song playlists in the future.