Kun lajin helppous ei viehätä: Rap-riimien arvattavuudesta ja alkusoinnuista

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(This post is written in Finnish since it discusses rhyme predictability and alliteration in Finnish rap lyrics.)

Tyypillisesti aloittelevan räppärin tunnistaa siitä, että käytetyt riimit ovat hyvin ennalta-arvattavia eli helppoja. Jos biisin jokainen laini päättyy samaa kaavaa noudattavaan kala-pala -tyyppiseen riimiin, jaksaa harva kuuntelija keskittyä kuuntelemaan kappaleen loppuun saakka, ellei tarina ole erityisen koukuttava. Tässä kirjoituksessa analysoidaan eri suomiräppäreiden lyriikoiden ennalta-arvattavuutta Raprofessor-pelissä annettujen vastausten perusteella. Lisäksi tarkastellaan erästä rap-lyriikoissakin käytettyä tehokeinoa, alkusointua, ja vertaillaan sen esiintyvyyttä eri artistien tuotannossa. Analyysi täydentää näin ollen aikasempaa Raplysaattori-ohjelmalla toteutettua riimikerroinvertailua. Continue reading →

Droppin’ Science on Haters

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My two rap-related projects, Raplyzer, which analyzes the rhyme density of different rappers, and DeepBeat, which is a rap lyrics generating AI, were widely covered in the media last year. But with the fame come the haters. The purpose of this post is to prove that my haters are wrong! (For real: I honestly don’t consider anyone a hater, nor will there be any proofs in this post. Rather, I’ll present some quantitative evidence for the validity of the algorithms but also discuss their limitations.)

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Who Let the DAGs Out?

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We did – in our paper titled “Beyond rankings: comparing directed acyclic graphs” (pdf) which I’ll be presenting at the ECML PKDD conference in Portugal next month. This was the first project of my PhD, but there’s also something else that makes it fundamentally different from the other research projects I’ve been involved with.

Typically, when I undertake a research project, I have a concrete question, like what is the next location a person will visit, to which I start looking for different solutions. In other words, I begin with a nail and start looking for a suitable hammer. However, this time we started by developing a cool new hammer with some neat theoretical properties before we had any idea if a suitable nail even exists. Continue reading →

Algorithm That Counts Rap Rhymes and Scouts Mad Lines

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“Men lie, women lie, numbers don’t” – Jay Z

Among the many things rappers like to boast about, some are relatively easy to quantify, like money, whereas rhyming skills are something that have been very difficult to measure – up till now. In this post, I’ll present Raplyzer, a computer program which automatically detects rhymes from rap lyrics and which is used to rank popular rappers based on their average Rhyme factor. I’ll also present another program called BattleBot, which is a search engine for rhyming rap lines based on the algorithm used in Raplyzer.

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Algoritmil voi saada kiinni / kenel on kovimmat rap-riimit

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“Puolet räppäreist ei tajuu rimmaamisest mitään / ennen mikkiin päästämistä pitäis kirjalliset pitää”

Näin toteaa suomiräpin epäilemättä tämän hetken tunnetuin nimi, Cheek, kappaleessaan Kuka muu muka. Tässä kirjoituksessa kuvailen, miten tietokoneella voidaan löytää lyriikoissa esiintyviä riimejä automaattisesti ja tutkin, löytyykö edellä mainitulle Cheekin väitteelle katetta analysoimalla Suomen tunnetuimpien räppäreiden sanoituksia toteuttamallani tietokoneohjelmalla. Ohjelma laskee tunnistamiensa riimien pituuksia sekä arvioi artistin sanavaraston kokoa. Continue reading →

Rappers and Physicists – What’s the Difference?

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Let me start with an easier question: rappers and physicists – what do they have in common? Apart from the fact that I’m moderately passionate about both rap and physics, there’s one obvious similarity: rappers and physicists are both very active collaborators. I’d say that rappers feature in each other’s songs much more often than artists of other genres do and physicists, on the other hand, have sometimes up to several hundreds of co-authors in their papers! Continue reading →