Signal Processing Toolbox



Signal Processing Toolbox

Perform signal processing and analysis

Machine Learning and Deep Learning for Signals

Perform preprocessing, feature engineering, signal labeling, and dataset generation for machine learning and deep learning workflows. Use the Signal Labeler app to create ground truth datasets and extract features to train AI models.

Signal Exploration and Preprocessing

Visualize, preprocess, and explore signals using the Signal Analyzer app. Denoise, smooth, and detrend signals to prepare them for further analysis.

Feature Extraction and Signal Measurements

Measure and extract distinctive features in signals including peaks, power, bandwidth, distortion, and signal statistics. Compute metrics related to pulses and transitions. Extract features for an entire dataset using the Signal Labeler app.

Filter Design and Analysis

Design, analyze, and implement digital and analog filters. Use the Filter Designer app or the Design Filter live editor task to design a variety of digital FIR and IIR filters, such as lowpass, highpass, and bandstop.

Spectral Analysis

Characterize the frequency content of a signal using spectral estimation and subspace techniques. Design, visualize, and implement windowing functions.

Time-Frequency Analysis

Visualize and compare time-frequency content of nonstationary signals using methods such as spectrogram, synchrosqueezing, and reassignment.

Vibration Analysis

Characterize vibrations in mechanical systems. Use order analysis to analyze and visualize spectral content occurring in rotating machinery. Perform experimental modal analysis and fatigue analysis.

Code Acceleration and Generation

Accelerate the execution of your signal processing algorithms using a Graphics Processing Unit (GPU). Generate portable C/C++ source code, standalone executables, or standalone applications from your MATLAB code.

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