Mathematical relationships underpin the arrangement of notes in a composition — and that makes music particularly open to AI-powered tools, which can be taught to recognise and create melodies and rhythms. Such tools are spreading at a rapid rate across all levels of the music industry, from streaming platforms and record labels to musicians and producers. Their adoption is provoking anxiety about mechanisation, but also enthusiasm for their transformative potential.
Applications of AI in music
STREAMING RECOMMENDATIONS
Streaming services such as Spotify use AI to map their users’ listening habits. By breaking down a person’s tastes according to the tempo or mood of their favourite music, deep-learning algorithms are able to build personalised recommendations for other songs. General listening trends are also analysed to customise playlists for users. Social media and internet sites are scoured for keywords about songs in order to build up profiles about how people are listening to them. For example, a song can be assigned a sadness rating, which can then be used to match it to a person who likes sad songs.
Sceptics complain that these algorithmically driven recommendations turn listeners into automatons. But their function is to open up huge song libraries such as Spotify’s catalogue of over 70mn tracks, which would otherwise be unnavigable for subscribers. Machine-learning algorithms are also used to enhance streaming quality by monitoring a user’s internet connection and tailoring the audio bitrate accordingly.