Signal Processing Toolbox
Signal Processing Toolbox provides functions that let you denoise, smooth, and detrend signals to prepare them for further analysis. For example, you can:
Resampling Signals (Example)
Change the sample rate of a signal without introducing artifacts.
Denoise signals with weighted moving average and Savitzky-Golay filters.
Outlier Removal (Example)
Remove spikes with median filters.
Normalize data and remove trends and offsets.
Aligning Signals with Different Start Times (Example)
Measure delays and align signals using cross-correlation.
Signal Generation (Example)
Generate pulses and swept-frequency signals (chirps, VCOs).
Signal Processing Toolbox provides functions that let you measure common distinctive features of a signal. Specifically, you can:
Signal Statistics (Example)
Measure minimum, maximum, mean, peak-to-peak amplitude, and RMS of a signal.
Pulse and Transition Metrics (Example)
Measure rise time, fall time, slew rate, overshoot, undershoot, settling time, pulse width, and duty cycle.
Find peak locations and measure peak height, prominence, and width.
Envelope Extraction (Example)
Extract the envelope of a signal using the Hilbert transform and the analytic signal.
Finding a Signal in a Measurement (Example)
Match a known signal in a measurement using cross-correlation techniques.
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).
Harmonic Distortion (Example)
Measure signal-to-noise ratio (SNR), total harmonic distortion (THD), and signal to noise and distortion ratio (SINAD).
Spurious-Free Dynamic Range (SFDR) (Example)
Analyze the effects of dithering in a numerically controlled oscillator (NCO).
Power, Bandwidth and Frequencies (Example)
Measure band power, bandwidth, and mean and median frequencies.
Instantaneous Frequency of Chirp (Example)
Measure the instantaneous frequency of a chirp using the analytic signal.
Data Reduction and Reconstruction (Example)
Reduce data dimension and provide fast signal reconstruction using the Walsh-Hadamard transform.
Fundamental Frequency Estimation (Example)
Estimate a speaker’s fundamental frequency in a speech signal using the complex cepstrum.
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:
Filter Design Gallery (Example)
Explore lowpass, highpass, bandpass, bandstop, differentiator, and arbitrary-magnitude frequency responses.
FIR Filter Design (Example)
Specify different filter design constraints, and compare FIR design algorithms such as Parks-McClellan (equiripple), least-squares, and Kaiser window.
IIR Filter Design (Example)
Compare magnitude and group delay responses of Butterworth, Chebyshev, and elliptic IIR filters.
Filter Design and Analysis Tool (FDATool) (Example)
Design digital filters interactively.
Filter Visualization Tool (FVTool) (Example)
Analyze digital filters in multiple domains.
Digital Filtering (Example)
Compensate for delay and distortion introduced by filters.
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:
Bias and Variability in the Periodogram (Example)
Lower PSD bias and variability using windowing and averaging with Welch and multitaper methods.
Lomb Periodogram (Example)
Estimate spectra of nonuniformly sampled signals or signals with missing samples.
Time-Frequency Analysis (Example)
Use spectrogram to determine when a frequency component is present in a signal, and explore time-frequency resolution tradeoffs.
Compare Frequencies of Two Signals (Example)
Estimate spectral coherence between signals, and measure relative phase between correlated frequency components.
Parametric Methods (Example)
Model short signals as outputs of autoregressive (AR) processes to achieve higher spectral resolution.
High-Resolution Methods (Example)
Estimate sinusoid frequencies in short signals using subspace methods, such as eigenvector and multiple signal classification (MUSIC).
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:
Formant Estimation with LPC Coefficients (Example)
Estimate vowel formant frequencies in a speech signal using linear predictive coding (LPC).
AR Order Selection with Partial Autocorrelation Sequence (Example)
Assess the order of an autoregressive model using the partial autocorrelation sequence.