Audio Analysis
Topic Overview Audio analysis is achieved by visually or programmatically inspecting local and global features in an audio signal in order to extract information or gain insight. Typical applications include understanding speech and speakers or analyzing music, environmental or wild life sounds. Together with optimized signal processing for time or frequency analysis as well as high-level machine learning and neural network capabilities, Wolfram Language provides solutions for applications in a variety of areas.
Audio Visualization
AudioPlot — waveform plot of audio
Spectrogram — spectrogram or time-frequency plot of audio
Periodogram — power spectrum plot of audio
Cepstrogram — power cepstra plot of audio
Understanding Speech »
SpeechRecognize — convert a spoken audio signal to text
SpeechCases ▪ SpeechInterpreter ▪ ...
Understanding General Audio Signals
AudioIdentify — attempt to identify what an audio signal is a recording of
PitchRecognize ▪ AudioInstanceQ
Analyzing Audio
AudioDistance — compute a distance measure between two audio objects
AudioBlockMap — apply a function to audio partitions
AudioLoudness — compute different loudness standards of an audio signal
AudioIntervals ▪ AudioMeasurements ▪ AudioLocalMeasurements
Frequency Analysis
ShortTimeFourier — compute short-time Fourier transform (STFT)
Fourier ▪ PeriodogramArray ▪ SpectrogramArray ▪ CepstrogramArray ▪ CepstrumArray ▪ InverseShortTimeFourier ▪ InverseSpectrogram
Audio Annotations
AudioAnnotate — annotate an audio object
AudioAnnotationLookup ▪ AnnotationDelete ▪ AnnotationRules
Machine Learning & Neural Networks »
Classify, Predict — create and apply classifiers or predictors to audio signals
Nearest ▪ FeatureNearest ▪ FeatureSpacePlot ▪ FindClusters ▪ ...
NetEncoder ▪ NetChain ▪ NetGraph ▪ ...
Models from the Wolfram Neural Net Repository »
"VGGish Feature Extractor Trained on YouTube Data" (feature extraction) ▪ "CREPE Pitch Detection Net Trained on Monophonic Signal Data" (pitch detection) ▪ …