IntelliTunes – Team Awesome

IntelliTunes – Predictive Analytics for Music Composition


The Problem

The music industry is notoriously fickle, and despite attempts to predict the next future chart topper, a significant amount of resources are devoted towards training and nurturing a group of artists with the hope that just a small percentage of them succeed. While this may seem like a challenge, the real challenge lies behind the scenes with the songwriters and musicians that come up with the tunes that we hear on the radio.


We believe that the music industry has seen a consistent genre shift every few years, from 70;s disco, to 80’s ballads, 90’s rock, 2000’s hip-hop and today’s Bieber. Given that we know a trend will last for a period of a few years, it would be worthwhile to invest in a system that would be able to predict these trends, and reduce the inefficiencies with song writing. Based on the type of music that’s on the top of the charts, and the historical chart toppers, we’re proposing an AI system that would be able to definitively compose a range of songs that would likely constitute future chart hits. Bieber would just be a mouthpiece for a significantly more intelligent machine.


Existing Platforms

There have been a few iterations of the proposed solution so far, but most of them have dealt with a library of past and present music, while IntelliTunes aims to be a predictive model of tomorrow’s music.

The Sony Computer Science Laboratory (“Sony CSL”) was probably the first commercial endeavour made to integrate AI and music composition. By analysing the musicality, tone, pitch and symphony in a range of music that was trending on top charts, the program was able to consolidate and create a unique pop song. It had all the markers from other top-ranking songs of the period, and hence should have also been a hit. However,much like Chef Watson, the result was something akin to serving caviar with peanut butter.

Platforms such as Pandora and Spotify also serve a need in the market by making an assessment of your future listening trends based on predictive analysis of your past music choices. While an astute use of AI, these platforms only serve to match your future listening needs with music that’s available on the market. It does not attempt to create songs that could be personalised for the individual listener.


The Proposed Solution

Much like how Chef Watson was the proposed AI solution to the culinary world, we expect IntelliTunes to be the the solution for the music industry. By design, IntelliTunes would be constantly consolidating the movements of songs on the charts, and identifying parameters such as time on the charts, sudden climbs, sudden drops and most importantly,region.


The program would utilise Deep Learning, which is particular type of machine learning whereby multiple layers of “neural networks” are programmed to process information between various input and output points – similar to a loose imitation of the human brain’s neural structure.This allows the AI platform to understand and model high-level abstractions in data, such as the patterns in a melody or the features in a person’s face.

The system would then be able to host a virtual library of micro-attributes of musicality, compose songs that would be expected to be desirable in the near future, given the movement and trends of today. An additional interesting application would be the ability to compose unique songs given a particular time period or genre for the listener that would like to create their own personal rendition of Metallica meets 40’s swing. Apps like Spotify have shown that giving users the ability to independently curate their music is a valuable proposition and creates especially sticky customers.


One of the great things about the proposed solution is that it would be language agnostic. IntelliTunes would be able to make predictions and compositions for songs across multiple geographies, because all it does it put together the notes to form a melody. Given the melody, a human songwriter will be able to piece in words with the pre-requisite amount of human emotion that an AI would not be able to replicate. An AI may have been able to come up to the tune of Nicki Minaj’s “Anaconda”, but it’s highly unlikely that it could have fathomed the lyrics.




Team Members:

Joseph Gnanapragasm, Cen Qian, Allison Weil &  Rachel Chamberlain