Jennifer Walshe

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.

Data sonification is a practice and genre in which numerical or categorical datasets are mapped to sound so that patterns in the data are perceived as musical structure. It differs from simple parameter automation by letting the data itself determine pitch, rhythm, dynamics, timbre, and spatial behavior through clearly defined mappings. Works range from direct audification of scientific signals (e.g., seismic or astronomical measurements) to carefully designed parameter mappings and model-based approaches that translate complex, multivariate datasets into layered sonic textures. Because it sits at the intersection of art, science, and design, data sonification is used both for aesthetic expression and for communicating or revealing structure in data that might be hard to see visually.

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.

Avant-garde music is an umbrella term for boundary-pushing practices that challenge prevailing norms of harmony, rhythm, timbre, form, and performance. It privileges experimentation, conceptual rigor, and a willingness to reframe what counts as music at all. Historically tied to early 20th‑century artistic modernism, avant-garde music introduced atonality, the emancipation of noise, and new forms of notation and process. It embraces indeterminacy, extended techniques, electronics, spatialization, and multimedia performance, treating sound as material to be sculpted, questioned, and reinvented.

Sound poetry is a vocal and performance-centered art in which the sonic qualities of language—phonemes, rhythm, timbre, breath, and noise—take precedence over conventional meaning. It treats the voice as an instrument, privileging articulation, texture, and extended vocal techniques (glossolalia, onomatopoeia, sputters, clicks, growls) rather than syntactic sense. Historically linked to the Futurist and Dada avant-gardes, it later embraced microphones, tape, and studio manipulation, blurring boundaries between poetry, music, theatre, and sound art. In performance, sound poetry ranges from minimal breath pieces to dense, rhythmically propelled vocal works, from solo utterance to ensemble structures and electroacoustic collages.