# create custom loss function for training
import keras.backend as K
def audio_discriminate_loss(gamma=0.1,num_speaker=2):
def loss_func(S_true,S_pred,gamma=gamma,num_speaker=num_speaker):
sum = 0
for i in range(num_speaker):
sum += K.sum(K.flatten((K.square(S_true[:,:,:,i]-S_pred[:,:,:,i]))))
for j in range(num_speaker):
if i != j:
sum -= gamma*K.sum(K.flatten((K.square(S_true[:,:,:,i]-S_pred[:,:,:,j]))))
loss = sum / (num_speaker*298*257*2)
return loss
return loss_func
def audio_discriminate_loss2(gamma=0.1,beta = 2*0.1,num_speaker=2):
def loss_func(S_true,S_pred,gamma=gamma,beta=beta,num_speaker=num_speaker):
sum_mtr = K.zeros_like(S_true[:,:,:,:,0])
for i in range(num_speaker):
sum_mtr += K.square(S_true[:,:,:,:,i]-S_pred[:,:,:,:,i])
for j in range(num_speaker):
if i != j:
sum_mtr -= gamma*(K.square(S_true[:,:,:,:,i]-S_pred[:,:,:,:,j]))
for i in range(num_speaker):
for j in range(i+1,num_speaker):
#sum_mtr -= beta*K.square(S_pred[:,:,:,i]-S_pred[:,:,:,j])
#sum_mtr += beta*K.square(S_true[:,:,:,:,i]-S_true[:,:,:,:,j])
pass
#sum = K.sum(K.maximum(K.flatten(sum_mtr),0))
loss = K.mean(K.flatten(sum_mtr))
return loss
return loss_func
