Synopsis:Scott decides that instead of writing natural history narratives about animal characters, he will write his own story: of how he went from growing up on a Berkshire farm in the UK, to training as a zoologist, to working as wildlife artist and safari guide in the Maasai Mara Nationa...
Furthermore, we use the adaptive cross-entropy loss function as the multi-task objective function, which automatically balances the learning of the multi-task model according to the loss proportion of each task during the training process. Therefore, the optimal weight combination can be found ...
The residual connection and batch normalization layer are added to the text encoder of the original Tacotron2, named the residual text encoder. The output of the residual text encoder is shown in Equation (1). Encoderres({𝒙𝑗}𝑁𝑗=1)=Encoder({𝒙𝑗}𝑁𝑗=1)+Phone_embedding(...
Referring to Figure 1, the 1-D convolutional block in the mask-estimation layer has two output paths: a residual path and a skip-connection path. The residual path acts as the input to the next block, while the skip-connection paths of all convolutional blocks are summed up as the output...
Additionally, in these approaches, it is hard to know if the model performed well due to a connection between modalities or if the generated image merely fitted the class features. The system we present (Figure 1) constitutes an end-to-end solution obtained after the training of an auto...
2.6. Training Objective In Multi-TALK, the loss function is represented by several weighted sums. First, the main loss is the speech-to-distortion ratio (SDR) [29,30] for estimating near-end speech in the time domain. The negative SI-SDR used for source separation [13] does not take in...