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   ▪ ...

AudioIdentify   ▪ SpeechRecognize   ▪ PitchRecognize   ▪ ...