# tf.contrib.signal.stft

tf.contrib.signal.stft(
signals,
frame_length,
frame_step,
fft_length=None,
window_fn=functools.partial(window_ops.hann_window, periodic=True),
name=None
)


See the guide: Signal Processing (contrib) > Computing spectrograms

Computes the Short-time Fourier Transform of signals.

Implemented with GPU-compatible ops and supports gradients.

#### Args:

• signals: A [..., samples] float32 Tensor of real-valued signals.
• frame_length: An integer scalar Tensor. The window length in samples.
• frame_step: An integer scalar Tensor. The number of samples to step.
• fft_length: An integer scalar Tensor. The size of the FFT to apply. If not provided, uses the smallest power of 2 enclosing frame_length.
• window_fn: A callable that takes a window length and a dtype keyword argument and returns a [window_length] Tensor of samples in the provided datatype. If set to None, no windowing is used.
• pad_end: Whether to pad the end of signals with zeros when the provided frame length and step produces a frame that lies partially past its end.
• name: An optional name for the operation.

#### Returns:

A [..., frames, fft_unique_bins] Tensor of complex64 STFT values where fft_unique_bins is fft_length // 2 + 1 (the unique components of the FFT).

#### Raises:

• ValueError: If signals is not at least rank 1, frame_length is not scalar, or frame_step is not scalar.