def stft_analysis(_input, window, N, H):
"""
Analysis of a sound using the short-time Fourier transform
Inputs:
_input: tensor of shape [batch_size, audio_samples]
window: analysis window, tensor of shape [N]
N: FFT size, Integer
H: hop size, Integer
Returns:
magnitudes, phases: 3D tensor with magnitude and phase spectra of shape
[batch_size, coefficients, frames]
"""
if (H <= 0):
raise ValueError("Hop size (H) smaller or equal to 0")
if not (is_power2(N)):
raise ValueError("FFT size is not a power of 2")
_input_shape = tf.shape(_input)
pad_size = int(N / 2)
with tf.name_scope('STFT_Zero_padding'):
zeros_left = tf.zeros(_input_shape)[:, :pad_size]
zeros_right = tf.zeros(_input_shape)[:, :pad_size]
_input = tf.concat([zeros_left, _input, zeros_right], axis=1)
with tf.name_scope('overlapping_slicer'):
sliced_input = overlapping_slicer_3D(_input, N, H)
_, frames, _ = sliced_input.get_shape()
with tf.name_scope('DFT_analysis'):
reshaped_sliced_input = tf.reshape(sliced_input, (-1, N))
m, p = dft_analysis(reshaped_sliced_input, window, N)
with tf.name_scope('STFT_output_reshape'):
magnitudes = tf.reshape(m, (-1, int(m.get_shape()[-1]), int(frames)))
phases = tf.reshape(p, (-1, int(p.get_shape()[-1]), int(frames)))
return magnitudes, phases
def dft_analysis(_input, window, N):
"""
Analysis of a signal using the discrete Fourier transform
inputs:
_input: tensor of shape [batch_size, N]
window: analysis window, tensor of shape [N]
N: FFT size
returns:
Tensors m, p: magnitude and phase spectrum of _input
m of shape [batch_size, num_coefficients]
p of shape [batch_size, num_coefficients]
"""
if not (is_power2(N)):
raise ValueError("FFT size is not a power of 2")
_, input_length = _input.get_shape()
_input_shape = tf.shape(_input)
if (int(input_length) > N):
raise ValueError("Input length is greater than FFT size")
if (int(window.get_shape()[0]) != N):
raise ValueError("Window length is different from FFT size")
if int(input_length) < N:
with tf.name_scope('DFT_Zero_padding'):
zeros_left = tf.zeros(_input_shape)[:, :int((N -
(int(input_length)) +
1) / 2)]
zeros_right = tf.zeros(_input_shape)[:, :int(
(N - (int(input_length))) / 2)]
_input = tf.concat([zeros_left, _input, zeros_right], axis=1)
assert (int(_input.get_shape()[1]) == N)
positive_spectrum_size = int(N / 2) + 1
with tf.name_scope('Windowing'):
window_norm = tf.div(window, tf.reduce_sum(window))
# window the input
windowed_input = tf.multiply(_input, window_norm)
with tf.name_scope('Zero_phase_padding'):
# zero-phase window in fftbuffer
fftbuffer_left = tf.slice(windowed_input, [0, int(N / 2)], [-1, -1])
fftbuffer_right = tf.slice(windowed_input, [0, 0], [-1, int(N / 2)])
fftbuffer = tf.concat([fftbuffer_left, fftbuffer_right], axis=1)
fft = tf.spectral.rfft(fftbuffer)
with tf.name_scope('Slice_positive_side'):
sliced_fft = tf.slice(fft, [0, 0], [-1, positive_spectrum_size])
with tf.name_scope('Magnitude'):
# compute absolute value of positive side
abs_fft = tf.abs(sliced_fft)
# magnitude spectrum of positive frequencies in dB
magnitude = 20 * log10(tf.maximum(abs_fft, 1E-06))
with tf.name_scope('Phase'):
# phase of positive frequencies
phase = angle(sliced_fft)
return magnitude, phase