Smoothing and Denoising
Remove unwanted spikes, trends, and outliers from a signal. Smooth signals using Savitzky-Golay filters, moving averages, moving medians, linear regression, or quadratic regression.
Apps
| Signal Analyzer | Visualize and compare multiple signals and spectra |
Functions
detrend | Remove polynomial trend |
filloutliers | Detect and replace outliers in data |
hampel | Outlier removal using Hampel identifier |
isoutlier | Find outliers in data |
medfilt1 | 1-D median filtering |
movmad | Moving median absolute deviation |
movmedian | Moving median |
sgolay | Savitzky-Golay filter design |
sgolayfilt | Savitzky-Golay filtering |
smoothdata | Smooth noisy data |
Topics
- Remove Trends from Data
Take out irrelevant overall patterns that impede data analysis.
- Remove the 60 Hz Hum from a Signal
Filter out 60 Hz oscillations that often corrupt measurements.
- Remove Spikes from a Signal
Use median filtering to eliminate unwanted transients from data.
- Reconstruct a Signal from Irregularly Sampled Data
Resample and interpolate data measured at irregular intervals.
- Eliminate Outliers Using Hampel Identifier
Detect and remove outliers using a simplified implementation of the Hampel algorithm.
Featured Examples
Signal Smoothing
Discover important patterns in your data while leaving out noise, outliers, and other irrelevant information.
Design and Analyze Savitzky-Golay Filters
Design, analyze, and apply Savitzky-Golay smoothing and differentiation filters, include weighting and optimization for best smoothness.
