syntheticsignal

A syntheticsignal is an artificially generated time-varying quantity designed to imitate the characteristics of real-world signals. It is produced by algorithms to provide controlled, repeatable inputs for analysis, testing, and training in fields such as communications, audio processing, instrumentation, and control systems.

Signals of this type can be deterministic, such as pure sinusoidal waves, chirps, square waves, or sums of multiple harmonics. They can also be stochastic, including colored or Gaussian noise, random walks, or other random processes. Often, synthetic signals combine deterministic structure with stochastic elements to resemble real data while preserving controllability over parameters like amplitude, frequency content, timing, and noise level.

Generation methods include mathematical models, digital signal processing techniques, and parameterized templates. Common approaches involve summing weighted sinusoids, applying time-varying envelopes, shaping spectra, or injecting controlled noise. Hybrid methods may replicate measured spectral characteristics and then add synthetic dynamics to produce realistic yet controllable signals. Documentation typically includes the seed for random generators, the exact model equations, and parameter values to ensure reproducibility.

Applications encompass testing and validating algorithms and hardware, sensor calibration, hardware-in-the-loop simulations, education and training, and data augmentation for machine learning. When using syntheticsignals, practitioners assess realism versus simplicity, document assumptions, and evaluate similarity to target signals using metrics such as spectral content, autocorrelation, and cross-correlation with reference signals.