Sonification: turning data into sound and music
Learning objectives
- learner can distinguish sonification from music while recognising the same audio can be both
- learner can explain inference-preserving mapping as the criterion for valid sonification
- learner can situate field-recording ethics in the sonification/sampling context
- learner can design a sound mapping that preserves conclusions about the source data
Capstone — one whole task that evidences the objectives
Design and document a small data sonification that maps a real dataset to sound using an inference-preserving mapping, with a written rationale distinguishing your sonification from a purely musical use of the same material and addressing recording ethics where relevant.
Prerequisite modules
Live coders routinely feed external data — sensor streams, weather APIs, financial tickers, archival field recordings — into Tidal Cycles or SuperCollider patterns. The question this module addresses is when that practice becomes sonification (a scientific or communicative act) versus when it stays music (an aesthetic act), and what obligations follow in either case. The distinction matters for how you present work publicly, write liner notes, and source your material ethically.
The scaffolding moves through three stations. First, the conceptual boundary: the atom on the sonification–music distinction establishes that the difference is not sonic but intentional — what question is the listener being invited to answer? The same Tidal pattern driven by climate data can be experienced as art or interrogated as evidence. Internalising this liberates you to work fluidly across both registers while knowing which you’re in.
Second, the design criterion: the inference-preserving mapping atom is the load-bearing technical concept. It defines what makes a mapping valid — conclusions a listener draws from the sound must correspond to conclusions about the source data — and introduces the order-zero/first-order/higher- order taxonomy of mappings. This is the conceptual core the capstone tests directly: you cannot write a convincing rationale without it, and you cannot design the mapping without knowing which order you’re operating at. Repeated practice drafting mappings against this criterion is why it appears as a part-task drill.
Third, ethics: the field-recording ethics atom anchors the recording-ethics clause of the capstone. Any sonification drawing on indigenous or field-recorded vocal material must address consent, attribution, and reciprocal benefit — the Midi Minds / Samburu case makes this concrete.
The supporting atom on audience reception of algorithmic music enriches the framing — it shows that listeners frequently assimilate data-driven sound into ordinary musical categories without detecting its computational origin — but the capstone does not depend on it. It is useful background for interpreting listener responses to your sonification, not a gate.
Atoms in this module
Required — these gate the capstone
Supporting — enrichment, not gating