colab版本
from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
本地版
from keras.layers.normalization.batch_normalization_v1 import BatchNormalization
def over_lap_and_add(self, framed_signals, frame_length, frame_step, winfunc=tf.signal.hamming_window):
"""overlap and add
params:
framed_signals: tf.float32, shape=[batch, n_frames, frame_length]
frame_length: Window length
frame_step: frame shift
return:
signals: tf.float32, shape=[batch, x_length]
"""
shape = tf.shape(framed_signals)
n_frames = shape[1]
# Generate de-overlapping windows
if winfunc is not None:
window = winfunc(frame_length, dtype=tf.float32)
window = tf.reshape(window, [1, frame_length])
window = tf.tile(window, [n_frames, 1])
window = tf.signal.overlap_and_add(window, frame_step)
signals = tf.signal.overlap_and_add(framed_signals, frame_step)
signals /= window
signals = tf.cast(signals, tf.float32)
return signals
SNR
标签:over,window,signals,add,tf,length,lap,frame From: https://www.cnblogs.com/prettysky/p/17004168.html