Signalrich

Signalrich is a theoretical metric used in signal processing and data analysis to quantify how richly a signal encodes information. It is not a standardized measure, and definitions vary among disciplines such as communications, neuroscience, and multimedia processing.

Signalrich aims to reflect how much of a signal's variation is informative rather than random or incidental. In practice, it is defined through a combination of features that may include energy, spectral entropy, sparsity, temporal dynamics, and statistical complexity. The exact formulation is not universal, and different implementations produce values on a common scale, often from 0 to 1 or 0 to 100.

Computation approaches vary. Some methods compare the informative energy to total energy, others incorporate entropy across time and frequency domains, and some rely on data-driven estimators trained to predict task performance as a proxy for information content. Preprocessing choices such as filtering, detrending, and normalization strongly influence the resulting score.

Applications of the concept include evaluating channel richness in communications, assessing neural or physiological signals in neuroscience, judging audio or video content richness, and guiding data compression or anomaly detection in sensor networks. Because of the lack of a universal standard, signalrich is typically used as a relative measure within a study rather than an absolute benchmark.

See also: information content, signal-to-noise ratio, spectral entropy, sparsity, complexity measures.