sincNET代码

sincNET 是用于语音信号处理的一种卷积神经网络结构，其主要特点是在卷积层中使用了 sinc 函数作为滤波器。

以下是使用 PyTorch 实现 sincNET 的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SincConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, sample_rate):
        super(SincConv1d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.sample_rate = sample_rate
        self.pi = torch.Tensor([3.14159265358979323846]).cuda()
        self.window = torch.hamming_window(kernel_size).cuda()
        self.freq_scale = torch.linspace(0, sample_rate / 2, out_channels).cuda()

        # 初始化滤波器权重和偏置
        self.weight = nn.Parameter(torch.zeros(in_channels, out_channels, kernel_size).cuda())
        self.bias = nn.Parameter(torch.zeros(out_channels).cuda())

        # 计算滤波器权重
        for c in range(out_channels):
            freq = self.freq_scale[c]
            b = torch.as_tensor([1.0 if i == kernel_size // 2 else 0.0 for i in range(kernel_size)])
            self.weight[:, c, :] = (2 * freq / self.sample_rate) * torch.sin(2 * self.pi * freq * (torch.arange(kernel_size).float() - kernel_size / 2) / self.sample_rate) * b
        self.weight.requiresGrad = True

    def forward(self, x):
        # sinc 卷积
        self.window = self.window.type_as(x)
        self.weight = self.weight.type_as(x)
        out = F.conv1d(x, self.weight, stride=1, padding=self.kernel_size // 2, dilation=1, groups=self.in_channels)
        out = out * self.window.view(1, 1, self.kernel_size).expand_as(out)
        out = F.max_pool1d(out, kernel_size=2, stride=2)

        # 添加偏置并使用激活函数
        out = out + self.bias.view(1, self.out_channels, 1).expand_as(out)
        out = torch.tanh(out)

        return out

class SincNet(nn.Module):
    def __init__(self, sample_rate):
        super(SincNet, self).__init__()
        self.sample_rate = sample_rate

        # 定义卷积层
        self.conv1 = SincConv1d(1, 80, 251, self.sample_rate)
        self.conv2 = nn.Conv1d(80, 60, 5, stride=1, padding=0)
        self.conv3 = nn.Conv1d(60, 60, 5, stride=1, padding=0)
        self.fc1 = nn.Linear(3000, 2048)
        self.fc2 = nn.Linear(2048, 2048)
        self.fc3 = nn.Linear(2048, 10)

    def forward(self, x):
        # 输入信号的大小为 (batch_size, 1, signal_length)
        x = self.conv1(x)
        x = self.conv2(x)
        x = F.max_pool1d(x, kernel_size=3, stride=3)
        x = F.relu(x)
        x = self.conv3(x)
        x = F.max_pool1d(x, kernel_size=3, stride=3)
        x = F.relu(x)

        # 将卷积层的输出展平并输入全连接层
        x = x.view(-1, 3000)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x = F.relu(self.fc2(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.fc3(x)

        return x

这里使用了 PyTorch 自带的 nn.Module 实现了 SincConv1d 和 SincNet 两个类，其中 SincConv1d 类实现了 sinc 卷积层，SincNet 类实现了整个 sincNET 网络。在 SincConv1d 中，使用了 nn.Parameter 定义了滤波器的权重和偏置，并在初始化函数中计算了滤波器权重。在 SincNet 类中，使用了 nn.Conv1d 和 nn.Linear 定义了其他卷积层和全连接层，并在 forward 函数中调用了 SincConv1d，最终输出分类结果。