![]() Brostep has more robotic sounds with a “metal-esque” aggression.īubble trance: Bubble trance is bright, upbeat trance music.Ĭatstep: This particularly-aggressive filthstep variation is promoted most enthusiastically by the label Monstercat.Ĭrustpunk: Crust punk has a fast, dirty sound influenced by anarcho-punk, hardcore punk, and extreme metal. Emerging in the ’80s, beatdown has slow, chugging breakdowns and later influenced the development of metalcore.īlack sludge: A combination of black metal and sludge, the music.īrostep: Brostep is a variation of dubstep that some view as “Americanized dubstep.” It emphasizes the middle register sounds as opposed to the sub-bass content that dubstep accentuates. From Australia.īeatdown: Beatdown is type of hardcore punk characterized by a more aggressive sound and vocals that are shouted, screamed, or growled. Aggrotech typically features distorted and pitch-shifted vocals, militant lyrics, and a fast, danceable beat.Īussietronica: It’s electronica. 50 Genres with the Strangest Names on SpotifyĪbstracto: It’s like complextro, but more abstract than rhythmic.Īggrotech: This is electronic music that fuses elements of electronic body music, industrial, noise, trance, and techno. Explore more new and interesting genres with this list of 1369 genres and counting, which is sorted sorted by familiarity. You can click them to hear what they sound like. So here, for a bit of fun with data and musical exploration, are some of the most strangely-named genres on Spotify. And then we plowed through in that order, plucking out the ones that sounded, well, strange to our ears, in terms of the music they were describing in English (see the full list for all of the genre names). We started with our list of 1369 genres of music (and growing), sorted by familiarity to bring up the more obscure ones at the bottom of the list. These are the secret rain forest dwellers and deep undersea creatures of the genre world. Let’s explore the most strangely-named music genres on Spotify. These genres emerge based on how it sounds, how people describe music, and how they listen to it. When you build a system for classifying music that reacts to cultural and acoustic information, some fairly strange-sounding clusters of music appear.
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