The present and future of post production business and technology

Can a computer Predict a Hit Movie or Song?

Can a computer Predict a Hit Movie or Song? If you mean profitability, yes. Fascinating use of neural networks.

This is a long, and not new, article that rambles through a fascinating story of how a lawyer, “Mr Pink”, “Mr Brown” and “Mr Bootstrap” collectively cracked the code for predicting the profitability of movies, TV shows and (separately) another team shows the likelihood of whether a song is going to be a hit.

The specifics of how they achieved both breakthroughs is interesting: have the computer software (usually some sort of neural net) analyze existing successes – music or movie.  It then analyzes new music or movie proposals to determine whether it is likley be be a hit (music) or how much money it will make at the box office (movie).

The head of hit-predictor Platinum Blue, Mike McCready claims:

Record executives have tended to be Humean: though they can tell you how they feel when they listen to a song, they don’t believe anyone can know with confidence whether a song is going to be a hit, and, historically, fewer than twenty per cent of the songs picked as hits by music executives have fulfilled those expectations. Platinum Blue thinks it can do better. It has a proprietary computer program that uses “spectral deconvolution software” to measure the mathematical relationships among all of a song’s structural components: melody, harmony, beat, tempo, rhythm, octave, pitch, chord progression, cadence, sonic brilliance, frequency, and so on. On the basis of that analysis, the firm believes it can predict whether a song is likely to become a hit with eighty-per-cent accuracy.

The emphasis is mine in case you didn’t pick that the software was four times more likely to predict a hit song than experienced record company executives – the people who make final decisions on these things! (Quite separately, how does anyone with a 20% success rate keep their job? Just asking.)

What is interesting is that it takes no “industry knowledge”:

McCready didn’t care about who the artist was, or the cleverness of the lyrics. He didn’t even have a way of feeding lyrics into his computer. He cared only about a song’s underlying mathematical structure.

Similarly the movie profitability prediction software was built without regard to “artistic” elements at all. It picks up on plot points, settings, and other aspects to determine – based entirely on what’s come before – how successful a movie will be.

Both technologies can be used to fine tune and re-predict the likelihood of a success. Now, I have all sorts of concerns about the application of the technology and how it might “force out” small, niche movies or music that wouldn’t make the hit status but would otherwise still be a satisfactory story and a profitable project.

In both instances it is metadata at the foundation of the input to the software: for music metadata about mathematic relationships within the music; for movies the details of plot points, setting, story, characters, etc. It’s another way that metadata makes the data more valuable by fine tuning the music or the plot/characters to make the project more profitable. Not exactly classic metadata!

By the way, the first half of the article I linked to above is the most salient – mostly in the middle. The end is a verbatim account of a meeting and doesn’t really go anywhere. I kept looking for the conclusion.

And don’t forget, if you’re interested in Metadata, I have a book – Conquering the Metadata Foundations of Final Cut Pro Xand an upcoming full day seminar at DV Expo – Using Metadata for Production and Asset Management coming up on September 21.



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