ref: e034b1096bf30eb290423746c2c7908424279ccf
parent: 05f02aaa49382193bfe66586aea37b7d7b2aa0a2
author: Jean-Marc Valin <jmvalin@amazon.com>
date: Fri Feb 25 08:55:21 EST 2022
Biasing for overestimating the pitch correlation
--- a/dnn/training_tf2/train_plc.py
+++ b/dnn/training_tf2/train_plc.py
@@ -104,7 +104,7 @@
e = (y_pred - y_true)*mask
e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')
bias_mask = K.minimum(1., K.maximum(0., 4*y_true[:,:,-1:]))
- l1_loss = K.mean(K.abs(e)) + alpha*K.mean(K.abs(e_bands) + bias*bias_mask*K.maximum(0., e_bands)) + K.mean(K.minimum(K.abs(e[:,:,18:19]),1.)) + 8*K.mean(K.minimum(K.abs(e[:,:,18:19]),.4))
+ l1_loss = K.mean(K.abs(e)) + 0.1*K.mean(K.maximum(0., -e[:,:,-1:])) + alpha*K.mean(K.abs(e_bands) + bias*bias_mask*K.maximum(0., e_bands)) + K.mean(K.minimum(K.abs(e[:,:,18:19]),1.)) + 8*K.mean(K.minimum(K.abs(e[:,:,18:19]),.4))
return l1_loss
return loss
--
⑨