noised

Noised is a term used to describe the deliberate introduction or presence of noise within a signal. It is employed across several disciplines to study, simulate, or exploit the effects of random variation in data and sound.

In signal processing, noising refers to injecting random data into a signal to test system robustness, model behavior under non-ideal conditions, or evaluate encoding and transmission performance. In psychoacoustics and auditory research, controlled noising helps measure masking effects and detection thresholds, contributing to the understanding of how noise influences perception. In machine learning, noising denotes data augmentation techniques that perturb inputs with random noise during training to improve model generalization and resilience to perturbations. In music and sound design, noising describes methods that embed noise textures into material, using sources such as white, pink, or brown noise, distortion, or granular synthesis to create timbral complexity.

History and practice vary by field, but the shared aim is to model, endure, or creatively exploit noise. Distinctions are often drawn between additive noising (inserting new random values) and multiplicative or structural noising (altering existing signals or dynamics). The concept has deep roots in early electronic music, communications theory, and statistical signal processing, and remains a common tool in both technical and artistic contexts.

See also: Dithering, Noise shaping, Data augmentation, White noise, Pink noise, Signal-to-noise ratio.