BMAT’s Melody Identification – Part 1: Music and Language

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See Part 2 – Running Out of Melodies

Years ago we investigated whether language and music were the same. In the process, we found out that the universe of tunes behaves as a social network and that western popular music might be running out of unique melodies.

Men sang before they talked. Apparently, our innate joy in repetition made our ancestors mimic the sounds of nature first (the roar of a beast, the whistle of a bird, the crying of a baby…), creating musical forms of expression long before they could modulate voice into words. It’s unclear at what point the creatures we descend from realised such singing could communicate ideas and feelings but it probably meant the major step in the creation of language.

Moving now to more contemporary and less hairy times, about 15 years ago, a bunch of us were at the MTG doing research on music and audio technologies. On those days we were easily excitable in almost every sense, also when exploring disciplines unknown to us. Some Complex Networks scientific publications had called our attention. They stated music and language were the same. Probably because we were good at playing instruments and bad at talking with people we thought the music language identity couldn’t be right and decided to take the matter into it.

Ha! We found that music, opposed to language, shared properties with other complex networks

We built different types of Complex Networks. For music, we took more than 10,000 western contemporary music scores covering a broad range of music styles and defined three different lexicons – note-note, note-duration, interval. For the note-note network, we considered notes as interacting units of a Complex Network. Accordingly, notes were nodes and a link existed between notes if they co-occurred in a music piece. The connection of this set of notes allowed constructing an unlimited number of melodies of which the collection of melodies of our corpus was a small sample. For the note-duration and the interval Complex Networks, we did pretty much the same.

Node and link representation for note-note, note-duration, and intervals.

In parallel, we built a set of Complex Networks for language, which resulted from the interaction of tri-phonemes, syllables and words in El Quijote by Cervantes. Similar to what we did with music, words, syllables and tri-phonemes were nodes and links were created at co-occurrence.

Complex Network MJ
The note-duration Complex Network obtained from Michael Jackson’s Billie Jean is graphed by default (left) and manually arranging the nodes corresponding to the same note (right).

We looked at results. The music networks displayed complex patterns different from the language. Ha! We found that music, opposed to language, shared properties with other complex networks, namely the small world phenomenon as well as a scale-free degree distribution. Just like social networks, where a friend of someone with a lot of friends will most probably have a lot of friends his own, the properties of the musical networks told us, whatever lexicon we choose to define the nodes, that most frequent melodic atoms belong to a very crowded and interconnected community while rare links belong to the ghettos.

“If the commercial music space is so confined, can we announce we are running out of popular melodies?”

While proving the music language identity wrong, we had accidentally proven that, at least for western commercial popular songs, uncommon melodies very rarely interact with the trendy. We also know that music typically sticks to dodecaphonic temperate scale and applies strict constraints over key, tempo and chroma to fit people’s ear pleasantness. If the commercial music space is so confined, can we announce we are running out of popular melodies? Could it be that melodies cannibalise each other? And is it possible that any melody identification technology – designed to match works and not sound recordings – is doomed to outcome “false” positives? Yes, yes, and “yes”. A bit more detailed replies in our part 2.

Àlex, CEO

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