Migro Records

Experimental music is an umbrella term for practices that prioritize exploration, process, and discovery over adherence to established genre norms. It embraces new sound sources, nonstandard tuning systems, indeterminacy and chance operations, graphic and open-form scores, extended techniques, and technology-led sound design (tape, electronics, computers, and live processing). Rather than a single style, it is a methodology and ethos: testing hypotheses about sound, structure, and performance, often blurring boundaries between composition, improvisation, sound art, and performance art. Listeners can expect unfamiliar timbres, unusual forms, and an emphasis on how music is made as much as the resulting sound.

Early music is a modern performance movement devoted to the repertoire of the Medieval, Renaissance, and early Baroque eras (roughly before c. 1750), approached with historically informed performance (HIP) practices. It emphasizes period instruments (or faithful replicas), historical tunings and temperaments, original notations and sources, and performance conventions documented in treatises of the time. The sound world ranges from monophonic chant and modal organum, to intricate Renaissance polyphony, to the emergence of basso continuo and dramatic affect in the early Baroque. Rather than being a single historical style, early music is a contemporary practice of reviving diverse pre‑Classical musics with scholarly rigor and artistic vitality.

AI (artificial intelligence) as a music genre refers to works in which machine-learning systems are central to generating, arranging, or performing core musical material, rather than being used only as peripheral studio tools. The style spans fully generative ambient soundscapes and pop songs written from text prompts, to voice-cloned performances and neural resynthesis of timbres. Stylistically it borrows from contemporary electronic and internet-born aesthetics (hyperpop, vaporwave, IDM, electropop), while foregrounding the uncanny, synthetic qualities of ML models. Beyond sonics, AI music is also a process-driven genre: datasets, prompts, model architectures, and iterative sampling are treated as creative choices on par with chords or instrumentation. Ethical and legal questions around training data, consent, and authorship are part of its identity and discourse.