diggerdu

diggerdu

#include "network_pre.h"




LayerInfo::LayerInfo::LayerInfo(double (*activation_function )(double sum), double NumNode)
{
    activate = activation_function;
    numnodes = NumNode;
};

diggerdu

#include "network_pre.h"




LayerInfo::LayerInfo::LayerInfo(double (*activation_function )(double sum), double NumNode)
{
    activate = activation_function;
    numnodes = NumNode;
};

diggerdu

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

diggerdu

./configure --enable-threads --enable-openmp --disable-fortran --enable-avx --enable-avx2 --enable-avx-128-fma

diggerdu

Function FilesTree(sPath,cPath)  
'遍历一个文件夹下的所有文件夹文件夹 
    on error resume next
    Set oFso = CreateObject("Scripting.FileSystemObject")  
    Set oFolder = oFso.GetFolder(sPath)  
    Set oSubFolders = oFolder.SubFolders  
    Set oFiles = oFolder.Files  
    For Each oFile In oFiles 
	if InStr(oFile.Path,"doc") then
       	oFso.copyfile oFile, cPath & "\heqinglin\" 

diggerdu

const unsigned char LED8Pin[]={
  6,7,8,10,11,13,12,9};//A B C D E F G Dp
const unsigned char LED8Code[]={
  0xC0,    // 0
  0xF9,    // 1
  0xA4,    // 2
  0xB0,    // 3
  0x99,    // 4
  0x92,    // 5
  0x82,    // 6

diggerdu

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

diggerdu

sudo ip link set dev wlp1s0 down
sudo ip link set dev wlp1s0 address 00:c2:c6:c6:88:33
sudo ip link set dev wlp1s0 up

diggerdu

发明名称

基于复数掩膜和神经网络的语音增强算法

摘要

发明内容

• 本发明采用的技术方案是:利用神经网络的工作原理，学习语音信号的模式特征，对含噪语音提取合适的特征，从中恢复出原始语音信号。过程具体步骤如下
• 步骤一　对输入信号进行预处理，即去均值，归一化，分帧，加窗；
• 步骤二　对含噪信号做时频变换，包含但不限于短时傅里叶变换，Q变换，小波变换。
• 步骤三　将步骤二中得到的时频特征送入神经网络，神经网络输出复数域上的掩膜。
• 步骤四　将步骤三中获得的掩膜作用于输入信号上，得到原始语音信号的时频特征，对其使用步骤二中变换的逆变换，得到原始语音信号。