Signal Processing Toolbox provides functions that let you denoise, smooth, and detrend signals to prepare them for further analysis. For example, you can:
Signal Processing Toolbox provides functions that let you measure common distinctive features of a signal. Specifically, you can:
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:
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:
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: