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AudioSpectrogram—Wolfram Language Documentation

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• See Also
  • NetEncoder
  • Audio
  • SpectrogramArray
  • AudioResample
  • ConformAudio
  • NetChain
  • NetGraph
  • NetTrain
  • Net Encoders
  • Audio
  • AudioSTFT
  • AudioMelSpectrogram
  • AudioMFCC
• Related Guides
  • Neural Networks
• Tech Notes
  • Neural Networks in the Wolfram Language

NetEncoder["AudioSpectrogram"]

represents an encoder that converts an audio file or object into its spectrogram.

NetEncoder[{"AudioSpectrogram","param"->val,…}]

represents an encoder with specific parameters for preprocessing and feature computation.

• The "AudioSpectrogram" encoder computes the spectrogram of a signal and discards some redundant information contained in the short-time Fourier transform. It also discards the phase information, which means that an exact reconstruction of the original signal is not possible.
• NetEncoder[…][input] applies the encoder to an input to produce a "Real32" NumericArray.
• NetEncoder[…][{input₁,input₂,…}] applies the encoder to a list of inputs to produce a list of NumericArray objects.
• The input to the encoder can be an Audio object or a File[…] expression.
• The output of the encoder is a rank-2 tensor of dimensions {n,Floor[(ws/2.)+1]}, where n is the number of partitions after the preprocessing is applied and ws is the length of the partitions used for the computation.
• An encoder can be attached to an input port of a net by specifying "port"->NetEncoder[…] when constructing the net.
Parameters
• The following general parameters are supported:
• "Augmentation" None augmentation to be applied "Normalization" None whether to apply normalization "SampleRate" 16000 target sample rate "TargetLength" All target output length
• Additional partitioning parameters:
• "WindowSize" Automatic length of the partitions "Offset" Automatic offset of the partitions "WindowFunction" Automatic window to be applied to the partitions
• The following settings and suboptions can be specified for each encoder parameter.
• "Normalization" can take the following settings:
• None no normalization "Max" absolute maximum value normalized to 1 {"Max",val} absolute maximum value normalized to val {"RMS",val} RMS of input audio signal normalized to val
• "TargetLength" can take the following settings:
• All same as input signal dur the duration dur specified as a time quantity n the first n partitions
• If the specified "TargetLength" does not match the length of the input signal, padding or trimming are applied accordingly.
• "Augmentation" can be specified as a list of rules with the following keys:
• "Convolution" None convolves an impulse response to the input "Noise" None adds noise to the input "TimeShift" None shifts the input by a specified amount "Volume" None multiplies the input with a constant
• Any augmentation parameter that accepts a numeric value can also be specified as a list of two numbers or a univariate distribution. In the first case, the value will be randomized according to a uniform distribution between the given bounds. In the second, the user-provided distribution will be used.
• Possible values for "Convolution" include:
• None no augmentation signal File or Audio object to be convolved with input {mix,signal} signal to be convolved with input and mix parameter
• Possible values for "Noise" include:
• None no augmentation amp white noise with amplitude amp noise File or Audio object containing the noise signal to be added {amp,noise}
• noise signal and its with the specified amplitude
• Use "TimeShift"->t to shift the input by t seconds, padding or trimming if necessary. Use Scaled[s] to shift the input by s×dur seconds, where dur is the duration of the input signal. Use {t₁,t₂} or Scaled[{ts₁,t₂}] to randomize the shift between the specified times.
• Use "Volume"->val to specify a constant multiplier.
• With the parameter "WindowSize"->Automatic, a partition length of 25 milliseconds is used. Use "WindowSize"->dur to select a partition length of duration dur. Use "WindowSize"->n to select a partition length of n samples.
• With the parameter "Offset"->Automatic, a partition offset of 8.33 milliseconds is used. Use "Offset"->dur to select a partition offset of duration dur. Use "Offset"->n to select a partition offset of n samples.
• Parameter "WindowFunction" applies a window to each partition. Possible settings are:
• None no windowing applied to the input audio Automatic func the window is computed using the function func list the sampled window list is explicitly specified

Create a spectrogram NetEncoder:

Create an Audio object:

Apply the encoder to the Audio object:

Plot the result:

NetEncoder["AudioSpectrogram"] can encode either File or Audio objects. Create a spectrogram encoder:

Apply the encoder to a File object:

Apply the encoder to an in-core Audio object:

Apply the encoder to an out-of-core Audio object:

Create a list of Audio objects:

NetEncoder["AudioSpectrogram"] maps across a batch of inputs:

Create a spectrogram NetEncoder:

Attach the encoder to the input of a net:

Apply the net to an Audio object:

Create an Audio object:

Use an encoder with "Normalization"->None to avoid any normalization:

Since the normalization is applied to the signal before the spectrogram is computed, there are no guarantees on the bounds of the result:

Use an encoder with "Normalization"->Automatic to normalize the maximum absolute value of the waveform samples to 1.:

Find the minimum and maximum values of the result:

Create an Audio object:

Using an encoder with "SampleRate"8000 resamples the signal to 8000Hz before performing the short-time Fourier transform:

The "SampleRate" parameter affects the computation of the default window size:

An encoder with a lower sample rate than the original audio will result in a shorter window length:

An encoder with a higher sample rate than the original audio will result in a longer window length:

Create an Audio object:

Using an encoder with "TargetLength"All returns the spectrogram for all the data:

Using an encoder with "TargetLength"10 zero-pads the output to be of length 10:

Using an encoder with "TargetLength"2 takes only the first two partitions:

Create an Audio object:

The partition length is automatically computed to be 25ms:

Using an encoder with "WindowSize"600 returns the spectrogram using partitions of 600 samples:

Create an Audio object:

The partition offset is automatically computed to be 1/3 of the partition length:

Using an encoder with "Offset"10 returns the short-time Fourier transform computed using partitions with an offset of 10 samples:

Create an Audio object:

Create a spectrogram NetEncoder:

The length of the result can be computed as Ceiling[length/offset], where length is the length of the signal after resampling and offset is the "Offset" parameter of the encoder:

Create an Audio object:

Create a spectrogram NetEncoder:

The second dimension of the result can be computed as Floor[windowSize/2+1], where windowSize is the "WindowSize" parameter of the encoder:

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