Wide scale penetration of smartphones, cheaper data and the advent of AI and other data science technologies are transforming the online music streaming industry like never before. Although the use of AI in music is not new, there are many significant developments and innovations in the journey forward.
The music industry has always been one of the early adopters of new technologies. From the days of vinyl records, cassettes, CDs, Walkman, pen drives and MP3 players to music streaming apps like Spotify, Pandora, Apple Music etc., — it is fascinating how technology has transformed the industry in the last few decades.
Grandview research estimates that global music streaming, which is valued at $20.9 billion in 2019, will expand at a compound annual growth rate (CAGR) of 17.8% from 2020 to 2027. The research cites the increasing popularity of digital platforms and wide scale adoption of smart devices as the key growth drivers during the forecast period. According to the RIAA, in 2019, music streaming accounted for 80% of the overall revenue generated by the recorded music industry in the US.
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AI and ML are decidedly changing the way music is created, accessed and consumed by users.
- Creating Music Using AI: Significant research is going on to leverage AI in music creation. Google’s Magenta is one such open-source research project that is exploring the role of machine learning in making music and art.
It involves manipulating source data of original songs, using this data to train AI/ML models, and create new content from these models. Nobody’s songs is an album composed with Magenta’s Music Transformer Neural Network. Similarly, legendary musician David Bowie partnered with Ty Roberts, founder of Gracenote, to create ‘Verbalizer’. Bowie then inputted up to 25 sentences and word groups into the Verbalizer tool to create lyrical combinations.
Sony CSL has launched FlowMachines, a new AI-driven music production that will integrate an AI-based recommendation system and human creativity to present the creator with melodies, chords, and bass best suited to their music taste. The tool will present the song creator with a different set of melodies based on the chord progression of the song they want to create.
Demixing a song is another application area for AI wherein a song can be deconstructed into various components. This will allow music producers to use a component of one song in another song.
- Listening to Music with AI: Music lovers are in for a special treat with the emergence of AI/ML. The days of searching for their favorite songs and adding to their playlists are over. Today all music streaming apps use Machine Learning algorithms to automatically create playlists for users based on their search and listening history. There are voice-assistants in every streaming app that plays songs on your commands, a cool feature that is particularly useful while driving or working. However, the real power of AI is yet to come.
Spotify is working on a new method of music discovery through an image-based song search. Spotify will allow users to take a photo and recommend them music based on the moment and mood. AI-driven DJ apps like Pacemaker are empowering users to mix and mash and create their own music.
- Music Distribution: AI and ML can also be used by song creators and music studios to distribute their content. AI records a lot of user data that provides deep insights into music users. The insights can be used to understand the pulse of the market and accordingly create content, run personalized ads to advertise your albums and songs. Already, many new artistes have benefited from reaching their audience through personalized recommendations.
The potential applications of AI and ML in music and online music streaming are huge. It is still at a nascent stage and a lot of work is being done to explore new ways AI/ML can help the entire ecosystem.