STFT - 17


  • name: STFT (GitHub)

  • domain: main

  • since_version: 17

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 17.


Computes the Short-time Fourier Transform of the signal.


  • onesided - INT (default is '1'):

    If onesided is 1, only values for w in [0, 1, 2, …, floor(n_fft/2) + 1] are returned because the real-to-complex Fourier transform satisfies the conjugate symmetry, i.e., X[m, w] = X[m,w]=X[m,n_fft-w]*. Note if the input or window tensors are complex, then onesided output is not possible. Enabling onesided with real inputs performs a Real-valued fast Fourier transform (RFFT).When invoked with real or complex valued input, the default value is 1. Values can be 0 or 1.


Between 2 and 4 inputs.

  • signal (heterogeneous) - T1:

    Input tensor representing a real or complex valued signal. For real input, the following shape is expected: [batch_size][signal_length][1]. For complex input, the following shape is expected: [batch_size][signal_length][2], where [batch_size][signal_length][0] represents the real component and [batch_size][signal_length][1] represents the imaginary component of the signal.

  • frame_step (heterogeneous) - T2:

    The number of samples to step between successive DFTs.

  • window (optional, heterogeneous) - T1:

    A tensor representing the window that will be slid over the signal.The window must have rank 1 with shape: [window_shape]. It’s an optional value.

  • frame_length (optional, heterogeneous) - T2:

    A scalar representing the size of the DFT. It’s an optional value.


  • output (heterogeneous) - T1:

    The Short-time Fourier Transform of the signals.If onesided is 1, the output has the shape: [batch_size][frames][dft_unique_bins][2], where dft_unique_bins is frame_length // 2 + 1 (the unique components of the DFT) If onesided is 0, the output has the shape: [batch_size][frames][frame_length][2], where frame_length is the length of the DFT.

Type Constraints

  • T1 in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16) ):

    Constrain signal and output to float tensors.

  • T2 in ( tensor(int32), tensor(int64) ):

    Constrain scalar length types to int64_t.