Signal processing

Signal processing covers the techniques used to analyze, clean, and transform signals that vary over time or space, such as sensor readings, audio waveforms, or vibration data. Its core operations include filtering out noise, decomposing a signal into its constituent frequencies, and detecting meaningful events embedded in a continuous stream of measurements.

A moving average is itself a basic signal-processing filter, smoothing out high-frequency noise while preserving a slower underlying trend; more advanced filters, along with techniques such as the Fourier transform, decompose a signal into the frequencies it is made of, making it possible to isolate a periodic component, such as a machine's rotation rate, from background noise. This differs from general time series forecasting, which predicts future values; signal processing is more often about understanding or cleaning the signal itself, before or instead of forecasting from it.

Signal processing matters in industrial IoT, audio and vibration monitoring, and any domain producing high-frequency sensor data, where raw readings are too noisy for direct analysis and depend on reliable sensor calibration upstream. It also underlies anomaly detection on sensor streams and relies on the same underlying concepts as autocorrelation for characterizing repeating patterns. A common pitfall is applying a filter tuned for one signal's frequency characteristics to a different one without adjustment, either smoothing away the real signal or letting noise through unfiltered.