Signal Processing
Topic Overview Signals are sequences over time and occur in many different domains, including technical (speed, acceleration, temperature, ...), medical (ECG, EEG, blood pressure, ...) and financial (stock prices, commodity prices, exchange rates, ...). Signal processing involves transforming and filtering signals to improve quality and extract information, as well as detecting events. The Wolfram Language has powerful signal processing capabilities, including digital and analog filter design, filtering, and signal analysis using the state-of-the-art algebraic and numerical methods that can be applied to any data.
Creating, Importing and Connecting to Signals »
Import — import data from standard formats
DeviceRead ▪ BinaryReadList ▪ "CSV" ▪ "EDF" ▪ "MP3" ▪ ...
Signal Transforms »
Fourier — compute the discrete Fourier transform of a signal
ShortTimeFourier ▪ LaplaceTransform ▪ DiscreteWaveletTransform ▪ ...
Visualization & Analysis »
Spectrogram — time-frequency analysis of a signal
Periodogram ▪ Histogram ▪ FindPeaks ▪ ...
Filtering & Filter Design »
ListConvolve — convolve a signal with a kernel
LowpassFilter ▪ MeanFilter ▪ RecurrenceFilter ▪ ...
LeastSquaresFilterKernel ▪ ButterworthFilterModel ▪ ToDiscreteTimeModel ▪ ...
Deploying & Exporting
MicrocontrollerEmbedCode — generate, compile and deploy code to microcontrollers
"FMU", "MO", ... — export filters and models
"CSV", "MP3", ... — export signals
Audio Processing & Analysis »
AudioPitchShift — shift the pitch of an audio signal
AudioReverb ▪ AudioLocalMeasurements ▪ AudioIntervals ▪ ...
Time Series Processing & Analysis »
MovingMap — apply a function to a moving overlapping window
TimeSeriesResample ▪ TimeSeriesAggregate ▪ Differences ▪ ...
Machine Learning for Signals »
Classify — classify a collection of signals
FindClusters ▪ FeatureSpacePlot ▪ NetModel ▪ NetTrain ▪ ...