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+4*wid:end-4*wid) * w_t; s_right_2 = s_right_0(1+wid:end-wid) + s_right_1(1+4*wid:end-4*wid) * 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);
时间: 2023-12-06 07:41:52 浏览: 28
这段代码是一个音频处理的迭代训练过程。具体来说,它包括以下几个步骤:
1. 初始化输入信号,其中 soud_left_d 和 soud_right_d 是左右声道的音频数据;
2. 使用 ResBlock 函数对输入信号进行残差块处理;
3. 对处理后的信号进行卷积操作,并加上一个权重 w_t,得到 s_left_2 和 s_right_2;
4. 对 s_left_2 和 s_right_2 进行像素洗牌(PixelShuffle)操作;
5. 再次对洗牌后的信号进行卷积操作;
6. 对信号进行归一化处理,使得其功率与参考信号 power_xr 相同;
7. 计算残差和损失,以及信噪比(SNR)。
整个过程会不断迭代,直到 SNR 达到一定阈值(46)。这种音频处理技术可以用于语音增强、降噪等方面。
相关问题
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。
if(snr_t < snr) w = w_t; conv_0 = conv_t; loss_num_t = loss_num; diff_t = diff; snr_t = snr; s_left_o = s_left_2; s_right_o = s_right_2; end conv_t = ParaRandom(conv_0,diff_t); w_t = w + w * 0.2 * (rand(1) - 0.5); end
这段代码看起来像是一个简单的优化算法,可能是用于信号处理或者通信系统中的自适应滤波器设计。其中,`w`是一个权重向量,`conv_0`是一个初始卷积核,`loss_num_t`是损失函数,`diff_t`是一个差分向量,`snr_t`是信噪比,`s_left_o`和_right_o`是左右信号。在每一次迭代中,如果当前的信噪比比之前得到的信噪比更好,则更新一些变量,否则使用随机参数进行优化。具体实现可能还需要结合其他代码来理解。