while(snr_t < 46 ) %迭代训练 % s_left_0 = conv(soud_left_d,conv_t(1,:)); % s_right_0 = conv(soud_right_d,conv_t(1,:)); s_left_0 = [fill,soud_left_d,fill]; s_right_0 = [fill,soud_right_d,fill]; s_left_1 = ResBlock(s_left_0,conv_t(2,:),conv_t(3,:)); s_right_1 = ResBlock(s_right_0,conv_t(2,:),conv_t(3,:)); s_left_1 = conv(s_left_1,conv_t(4,:)); s_right_1 = conv(s_right_1,conv_t(4,:));

这段代码是用于迭代训练的，其中的`while`循环条件是当信噪比`snr_t`小于46时执行。接下来的代码中，首先使用`conv_t(1,:)`对左右声道的信号进行卷积操作，并将结果存储到`s_left_0`和`s_right_0`变量中。然后，在`s_left_0`和`s_right_0`两个变量的首尾分别添加`fill`填充值。接着，调用`ResBlock`函数对`s_left_0`和`s_right_0`进行残差块处理，得到`s_left_1`和`s_right_1`。最后，再次对`s_left_1`和`s_right_1`进行卷积操作，并将结果存储到`s_left_1`和`s_right_1`变量中。

相关问题

while(snr_t < 46 ) %迭代训练 s_left_0 = [fill,soud_left_d,fill]; s_right_0 = [fill,soud_right_d,fill]; s_left_1 = ResBlock(s_left_0,conv_t(2,:),conv_t(3,:)); s_right_1 = ResBlock(s_right_0,conv_t(2,:),conv_t(3,:)); s_left_1 = conv(s_left_1,conv_t(4,:)); s_right_1 = conv(s_right_1,conv_t(4,:)); s_left_2 = s_left_0(1+wid:end-wid) + s_left_1(1+4wid:end-4wid) * w_t; s_right_2 = s_right_0(1+wid:end-wid) + s_right_1(1+4wid:end-4wid) * w_t; s_left_2 = PixelShuffle(s_left_2,conv_t(6:21,:)); s_right_2 = PixelShuffle(s_right_2,conv_t(6:21,:)); s_left_2 = conv(s_left_2,conv_t(5,:)); s_right_2 = conv(s_right_2,conv_t(5,:)); s_left_2 = s_left_2(1+wid:end-wid); s_right_2 = s_right_2(1+wid:end-wid); power_s = Count(s_left_2(1,:)); power_mult = power_s / power_xr; s_left_2 = s_left_2 / (power_mult)^0.5; s_right_2 = s_right_2 / (power_mult)^0.5; temp = s_left_2 - soud_left_t; loss_num = LossCount(temp,soud_left); diff = Variance(temp,soud_left); snr = psnr(temp,soud_left);

这段代码的作用是对语音信号进行迭代训练和处理，希望获得更高的信噪比（SNR）。具体来说，它首先将输入的左右声道信号进行填充，然后通过ResBlock函数进行残差块的处理。接着，通过卷积操作对信号进行处理，并使用PixelShuffle函数对信号进行下采样。最后，通过计算信号的功率、多项式系数等参数，对信号进行归一化处理，并计算损失函数、方差和信噪比等指标。这个过程会不断迭代，直到信噪比达到指定的阈值。

conv_t = ParaRandom(conv_0,diff_t); %改变卷积核参数 w_t = w + w * 0.2 * (rand(1) - 0.5); num = num + 1; if(isempty(target_psnr) == 1) judge = (num < target_num); elseif(target_num == 0) judge = (snr_t < target_psnr); else judge = (snr_t < target_psnr) + (num < target_num); end

这段代码看起来像是针对图像处理中的卷积核参数进行随机化和调整的过程。具体来说，该代码使用ParaRandom函数随机改变了卷积核的参数，然后根据一定的条件进行调整。

其中，w_t = w + w * 0.2 * (rand(1) - 0.5)这行代码应该是用来对参数进行微调的，w表示原始参数，rand(1)生成一个随机数，0.2表示微调的幅度，-0.5表示将随机数变为-0.5到0.5之间的值。最终得到的w_t就是微调后的新参数。

接下来的if语句看起来是判断是否达到了目标PSNR或目标迭代次数。如果没有设置目标PSNR，则只考虑目标迭代次数；如果设置了目标PSNR，则需要同时满足目标PSNR和目标迭代次数。如果满足其中一个条件，则退出循环。