STFT¶

STFT - 17¶

Version¶

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.

Summary¶

Computes the Short-time Fourier Transform of the signal.

The STFT is computed by sliding a window of length frame_length over the signal with a step size of frame_step, computing a DFT of each windowed frame.

The number of frames in the output is computed as:

frames = floor((signal_length - frame_length) / frame_step) + 1

Constraints on inputs:

frame_step must be a scalar.
frame_length must be a scalar. When omitted and window is provided, frame_length is inferred from window.shape[0]. When both window and frame_length are omitted, frame_length defaults to signal_length.
window must be a 1-D tensor. When omitted, a rectangular (all-ones) window of length frame_length is used. When both window and frame_length are provided, the length of the window tensor must equal frame_length.

Attributes¶

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, 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.

Inputs¶

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. The tensor is expected to have rank 3.
frame_step (heterogeneous) - T2:

A scalar representing the number of samples to step between successive DFTs.
window (optional, heterogeneous) - T1:

An optional 1-D tensor representing the window function to be applied to each frame of the signal before computing the DFT. The length of the window (window.shape[0]) determines the frame length when frame_length is not specified. If both window and frame_length are provided, the length of the window must equal frame_length. When omitted, a rectangular (all-ones) window of length frame_length is used.
frame_length (optional, heterogeneous) - T2:

An optional scalar representing the length of each frame (i.e., the DFT size). When omitted and window is provided, frame_length is inferred from window.shape[0]. When both window and frame_length are omitted, frame_length defaults to signal_length. If both frame_length and window are provided, the length of the window must equal frame_length.

Outputs¶

output (heterogeneous) - T1:

The Short-time Fourier Transform of the signal. The number of frames in the output is frames = floor((signal_length - frame_length) / frame_step) + 1. 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. The last dimension of size 2 represents the real and imaginary parts of each complex value.

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.

Examples¶

default¶

import numpy as np
import onnx

signal = np.arange(0, 128, dtype=np.float32).reshape(1, 128, 1)
length = np.array(16).astype(np.int64)
onesided_length = (length >> 1) + 1
step = np.array(8).astype(np.int64)

no_window = ""  # optional input, not supplied
node = onnx.helper.make_node(
    "STFT",
    inputs=["signal", "frame_step", no_window, "frame_length"],
    outputs=["output"],
)

nstfts = ((signal.shape[1] - length) // step) + 1
# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
    start = i * step
    stop = i * step + length
    complex_out = np.fft.fft(signal[0, start:stop, 0])[0:onesided_length]
    output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)

output = output.astype(signal.dtype)
expect(node, inputs=[signal, step, length], outputs=[output], name="test_stft")

node = onnx.helper.make_node(
    "STFT",
    inputs=["signal", "frame_step", "window"],
    outputs=["output"],
)

# Test with window
a0 = 0.5
a1 = 0.5
window = a0 + a1 * np.cos(
    2 * np.pi * np.arange(0, length, 1, dtype=np.float32) / length
)
nstfts = 1 + (signal.shape[1] - window.shape[0]) // step

# [batch_size][frames][frame_length][2]
output = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
    start = i * step
    stop = i * step + length
    complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[
        0:onesided_length
    ]
    output[0, i] = np.stack((complex_out.real, complex_out.imag), axis=1)
window = window.astype(signal.dtype)
output = output.astype(signal.dtype)
expect(
    node,
    inputs=[signal, step, window],
    outputs=[output],
    name="test_stft_with_window",
)