Machine-Learning Algorithm Mines Rap Lyrics, Then Writes Its Own

Machine-Learning Algorithm Mines Rap Lyrics, Then Writes Its Own

From the post:

The ancient skill of creating and performing spoken rhyme is thriving today because of the inexorable rise in the popularity of rapping. This art form is distinct from ordinary spoken poetry because it is performed to a beat, often with background music.

And the performers have excelled. Adam Bradley, a professor of English at the University of Colorado has described it in glowing terms. Rapping, he says, crafts “intricate structures of sound and rhyme, creating some of the most scrupulously formal poetry composed today.”

The highly structured nature of rap makes it particularly amenable to computer analysis. And that raises an interesting question: if computers can analyze rap lyrics, can they also generate them?

Today, we get an affirmative answer thanks to the work of Eric Malmi at the University of Aalto in Finland and few pals. These guys have trained a machine-learning algorithm to recognize the salient features of a few lines of rap and then choose another line that rhymes in the same way on the same topic. The result is an algorithm that produces rap lyrics that rival human-generated ones for their complexity of rhyme.

The review is a fun read but I rather like the original paper title as well: DopeLearning: A Computational Approach to Rap Lyrics Generation by Eric Malmi, Pyry Takala, Hannu Toivonen, Tapani Raiko, Aristides Gionis.

Abstract:

Writing rap lyrics requires both creativity, to construct a meaningful and an interesting story, and lyrical skills, to produce complex rhyme patterns, which are the cornerstone of a good flow. We present a method for capturing both of these aspects. Our approach is based on two machine-learning techniques: the RankSVM algorithm, and a deep neural network model with a novel structure. For the problem of distinguishing the real next line from a randomly selected one, we achieve an 82 % accuracy. We employ the resulting prediction method for creating new rap lyrics by combining lines from existing songs. In terms of quantitative rhyme density, the produced lyrics outperform best human rappers by 21 %. The results highlight the benefit of our rhyme density metric and our innovative predictor of next lines.

You should also visit BattleBot (a rap engine):

BattleBot is a rap engine which allows you to “spit” any line that comes to your mind after which it will respond to you with a selection of rhyming lines found among 0.5 million lines from existing rap songs. The engine is based on a slightly improved version of the Raplyzer algorithm and the eSpeak speech synthesizer.

You can try out BattleBot simply by hitting “Spit” or “Random”. The latter will randomly pick a line among the whole database of lines and find the rhyming lines for that. The underlined part shows approximately the rhyming part of a result. To understand better, why it’s considered as a rhyme, you can click on the result, see the phonetic transcriptions of your line and the result, and look for matching vowel sequences starting from the end.

BTW, the MIT review concludes with:

What’s more, this and other raps generated by DeepBeat have a rhyming density significantly higher than any human rapper. “DeepBeat outperforms the top human rappers by 21% in terms of length and frequency of the rhymes in the produced lyrics,” they point out.

I can’t help but wonder when DeepBeat is going to hit the charts! 😉

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