Deezer trained the AI using raw audio signals, linguistic context reconstruction models and a Million Song Dataset that aggregates Last.fm tags describing tunes (such as "calm" or "sad"). The researchers mapped the MSD to Deezer's library using song metadata, extracting individual words from the lyrics in the process. The result was an 18,644-song database the team could use to both train AI on song moods and to test its theories. The system is merely average at detecting the mood of a song based on lyrics. However, the association between audio and lyrics helped it gauge the energy of a given piece more effectively than past techniques. This could help identify the difference between a soothing downtempo piece and an upbeat dance track, as an example. This isn't ready for use in services like Deezer. The research group wants to look at different training models (such as an unsupervised system that looks at huge volumes of unlabeled info) to improve the accuracy. You can see where this might go, mind you. Deezer could automatically generate playlists that cater to a wide variety of moods without having to tag every song by hand. You could listen to songs that are just upbeat enough to improve your spirits, or slow enough to set you at ease without putting you to sleep.
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.