- Signal transforms including fast Fourier transform (FFT), short-time Fourier transform (STFT), and Hilbert transform
- FIR and IIR filter design and analysis
- Measurements such as transition and pulse metrics, band power, bandwidth, and distortion
- Power spectrum estimation algorithms and data windowing functions
- Statistical signal measurements such as autocorrelation and cross-correlation
- Linear prediction and parametric time series modeling
- Waveform and pulse generation and data resampling functions

Signal Processing Toolbox provides functions that let you denoise, smooth, and detrend signals to prepare them for further analysis. For example, you can:

- Remove noise, outliers, and spurious content from data
- Enhance signals, visualize signals, and discover patterns
- Change the sample rate of a signal or make the sample rate constant for irregularly sampled signals or signals with missing data
- Generate synthetic signals such as pulses and chirps for simulation and algorithm testing

Signal Processing Toolbox provides functions that let you measure common distinctive features of a signal. Specifically, you can:

- Extract key signal characteristics and reduce data sets without losing information
- Locate signal peaks and determine their height, width, and distance to neighbors
- Measure time-domain features such as peak-to-peak amplitudes and signal envelopes
- Measure pulse metrics such as overshoot and duty cycle

In the frequency domain, you can measure fundamental, mean, median, and harmonic frequencies, as well as channel bandwidth and power in a frequency band. This toolbox lets you characterize systems by measuring spurious free dynamic range (SFDR), signal-to-noise ratio (SNR), total harmonic distortion (THD), signal to noise and distortion ratio (SINAD), and third-order intercept point (TOI).

Use the functions and apps within Signal Processing Toolbox to design, analyze, and implement a variety of digital FIR and IIR filters, such as lowpass, highpass, and bandstop. With these functions and apps, you can:

- Visualize magnitude, phase, group delay, impulse, and step responses
- Examine filter poles and zeros
- Evaluate filter performance by testing stability and phase linearity
- Apply filters to data and remove delays and phase distortion using zero-phase filtering

Signal Processing Toolbox provides functions for analog filter design and analysis. Supported analog filter types include Butterworth, Chebyshev, Bessel, and elliptic. The toolbox also contains discretization functions, such as the impulse invariance and bilinear transformation methods for analog-to-digital filter conversion.

Characterize the frequency content of a signal using the family of spectral analysis functions and apps within Signal Processing Toolbox. FFT-based nonparametric methods, such as Welch's method or the periodogram, make no assumptions about the input data and can be used with any kind of signal. Parametric and subspace methods, such as Burg's, Yule-Walker, and MUSIC, incorporate prior knowledge of the signal and can yield more accurate spectral estimates. With these functions and apps, you can:

- Compute power spectra of nonuniformly sampled signals or signals with missing samples using the Lomb-Scargle method
- Analyze nonstationary signals using time-frequency techniques such as the spectrogram, and measure signal similarities in the frequency domain by estimating spectral coherence
- Design and analyze Hamming, Kaiser, Gaussian, and other windows

Signal Processing Toolbox provides parametric modeling techniques that let you estimate a rational transfer function describing a signal, system, or process. To do this, you would:

- Use known information about a signal to find the coefficients of a linear system that models it
- Approximate a given time-domain impulse response using Prony and Steiglitz-McBride ARX models
- Find an analog or digital transfer function that matches a given complex frequency response
- Model resonances using linear prediction filters