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.

Attributes

onesided: 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. Default value is 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.
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.

Outputs

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.

Examples

default

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)

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 * 3.1415 * 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)
expect(
    node,
    inputs=[signal, step, window],
    outputs=[output],
    name="test_stft_with_window",
)

STFT#

STFT - 17#

STFT - 17 #