Signal Processing
Algorithms that analyze, transform, or filter signals.
Signal processing algorithms work with sampled data such as audio, sensor readings, time series, and images.
Convolution
Combines two signals or arrays by sliding one over the other and summing pointwise products.
Discrete Fourier Transform
Represents a finite sequence as weighted sums of discrete complex sinusoids.
Kalman Filter
Estimates hidden state by blending predictions with noisy measurements.
Moving Average
Smooths a sequence by replacing each value with an average over a nearby window.
Autocorrelation
Measures how similar a signal is to shifted versions of itself.
High-Pass Filter
Attenuates low-frequency signal components while preserving rapid changes.
Inverse Fast Fourier Transform
Reconstructs time-domain samples from frequency-domain coefficients efficiently.
Low-Pass Filter
Attenuates high-frequency signal components while preserving slower variations.
Median Filter
Replaces each sample or pixel with the median of a local neighborhood.
Short-Time Fourier Transform
Analyzes frequency content over short moving windows of time.
Windowing
Shapes a signal segment before frequency analysis to reduce edge artifacts.
Butterworth Filter
Filters signals with a smooth passband and monotonic frequency response.
Savitzky-Golay Filter
Smooths data by fitting a low-degree polynomial inside a moving window.
Exponential Moving Average
Smooths a sequence by blending each new value with the previous smoothed value.
Goertzel Algorithm
Efficiently measures one selected frequency bin from a signal.
Phase Vocoder
Changes time scale or pitch by manipulating short-time Fourier transform frames.
Overlap-Add
Reconstructs or filters long signals by summing overlapping processed blocks.
FIR Filter
Filters a signal using a finite weighted sum of current and previous input samples.
Chebyshev Filter
Designs a frequency filter with controlled ripple and a sharper transition band.
IIR Filter
Filters a signal with feedback from previous outputs as well as current and previous inputs.
Wavelet Transform
Represents a signal at multiple scales using localized wavelet functions.
Continuous Wavelet Transform
Analyzes a signal across continuously varying scales and shifts.
Hilbert Transform
Builds an analytic signal by phase-shifting frequency components.
Cepstrum Analysis
Analyzes periodic structure by transforming the log spectrum back into a quefrency-like domain.
Spectral Subtraction
Reduces noise by subtracting an estimated noise spectrum from the observed spectrum.
Overlap-Save
Performs long convolution by reusing overlapped input blocks and discarding invalid edges.
Polyphase Resampling
Resamples a signal efficiently by splitting interpolation filters into phases.
Windowed Sinc Interpolation
Interpolates samples with a sinc kernel limited by a window function.
LMS Adaptive Filter
Adapts filter weights to minimize the current squared error.
Notch Filter
Suppresses a narrow frequency band while leaving nearby frequencies mostly intact.
Band-Pass Filter
Passes frequencies inside a chosen band while attenuating lower and higher frequencies.
Matched Filter
Detects a known signal pattern by correlating input with a matching template.
Bessel Filter
Bessel Filter filters, models, or extracts features from sampled signals.
Elliptic Filter
Elliptic Filter filters, models, or extracts features from sampled signals.
Comb Filter
Comb Filter filters, models, or extracts features from sampled signals.
All-Pass Filter
All-Pass Filter filters, models, or extracts features from sampled signals.
Envelope Detection
Envelope Detection filters, models, or extracts features from sampled signals.
Zero-Crossing Rate
Zero-Crossing Rate filters, models, or extracts features from sampled signals.
Mel-Frequency Cepstral Coefficients
Mel-Frequency Cepstral Coefficients filters, models, or extracts features from sampled signals.
Linear Predictive Coding
Linear Predictive Coding filters, models, or extracts features from sampled signals.
Yule-Walker Method
Yule-Walker Method filters, models, or extracts features from sampled signals.
Wiener Deconvolution
Wiener Deconvolution filters, models, or extracts features from sampled signals.
Levinson-Durbin Recursion
Solves Toeplitz linear systems from autocorrelation values using a stable recursive update.
Burg's Method
Burg's Method addresses autoregressive spectral estimation by estimating reflection coefficients by minimizing forward and backward prediction errors.