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Qt实战：自定义声音波形图控件
在这篇文章中，我们将探索如何使用Qt创建自定义声音波形图控件。这是一个非常有用的控件，可以用于显示音频文件中的波形数据，或者用于实时显示来自麦克风的音频。
首先，我们需要创建一个Qt项目，并添加一个自定义控件类。在这个类中，我们将重载paintEvent()函数，以便能够绘制波形图。
下面是示例代码：
class SoundWaveWidget : public QWidget
{
Q_OBJECT
public:
SoundWaveWidget(QWidget *parent 毕业设计之基于Qt数字音频处理软件设计
把之前用到的收藏的博客或者资料链接，挂在这里，以备后用（也为了腾开收藏夹…） Qt开源社区 https://www.qter.org/ fmod库实现QQ变声特效 https://www.jianshu.com/p/9d1a3429badc B站Qt入门视频 https://www.bilibili.com/video/BV1g4411H78N?p=4 关于ffmpeg ffmpeg和QT开发播放器之音频库使用 https://blog.youkuaiyun.com/tainjau/article/details
声音波形图-Wavedata QT：利用QT实现波形曲线绘制
在主函数中，我们创建了一个QApplication对象和一个WaveWidget对象，并展示出WaveWidget的窗口。Wavedata QT是一款基于QT框架开发的声音波形图绘制工具，可以帮助用户快速地生成声音波形图，便于分析和处理声音数据。使用Wavedata QT需要事先安装QT开发环境，并下载Wavedata QT的源代码。调用addData函数即可将采样点添加到声音波形图中。综上所述，Wavedata QT是一款方便易用的声音波形图绘制工具，通过以上介绍和代码实现，相信读者可以轻松上手使用。
Qt/C++编写自定义控件60-声音波形图
一、前言 这个控件源自于一个音乐播放器，在写该音乐播放器的时候，需要将音频的数据转换成对应的频谱显示，采用的fmod第三方库来处理（fmod声音系统是为游戏开发者准备的革命性音频引擎，非常强大和牛逼），fmod负责拿到音频数据对应的采样频谱数据，然后传给这个控件进行绘制即可，本控件主需要专注于绘制即可，这样fmod对应封装的类专注于音频采集等处理，实现了隔离，修改和增加功能比较方便，声音波形图控件...
06波形曲线-06声音波形图-wavedata
一、运行效果 二、功能特点 可设置采样深度。 可设置当前位置线条宽度、线条颜色。 可设置背景颜色。 可设置三条数据曲线的颜色。 可设置数据展示样式。 支持最多3条数据曲线。 三、公共接口 public Q_SLOTS: //设置采样深度 void setDeep(double deep); //设置是否显示线条+线条宽度+线条颜色 void setShowLine(bool showLine); void setLineWidth(int lineWidth)
Qt显示WAV音频文件的波形图频谱图.rar
Qt显示WAV音频文件的波形图频谱图 https://blog.youkuaiyun.com/yulinxx/article/details/110651967
qt可视化显示音频波形程序代码.zip
qt可视化显示音频波形程序代码
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Python 爬取网页文字并保存为 txt 文件教程
将要爬取的网页网址赋值给变量url。
Plecs standalone 4.9.5最新版本，赶紧来下载使用吧，需要做并网逆变器及光伏储能变换器的朋友赶紧下载起来
PLECS 4.9.5较4.7.1版实现全面升级：采用自适应求解器切换技术，支持多核CPU/GPU加速，仿真速度提升30-50%；新增模型引用功能，实现模块化设计；强化光伏/风电等新能源组件，支持多物理场耦合分析；优化代码生成功能，兼容多种硬件平台和FMU导入。新版特别适用于复杂电力电子系统、多域联合仿真及硬件在环测试项目，但对硬件配置要求更高。基础教学项目仍可使用4.7.1版本。建议用户根据项目复杂度选择升级方案。
020、Sensor 供电设计：AVDD、DVDD、IOVDD 的上电时序与纹波要求
本文总结了图像传感器（Sensor）供电设计中的关键要点与常见问题：1. 三路供电特性差异：AVDD（模拟供电）对纹波最敏感（通常<30mVpp），DVDD需应对大电流瞬态变化，IOVDD需满足MIPI接口电压稳定要求。2. 上电时序至关重要，应遵循"模拟→数字→IO"的启动顺序，时序偏差可能导致初始化失败。3. 纹波控制需合理选择电容组合（如10μF+0.1μF），注意布局走线及电容ESR/ESL特性。4. 调试建议使用示波器多通道测量时序和纹波，重点关注传感器启动瞬间的电源特性。5. 经验教训包括预留设
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
TMS_WebGMaps_1.9.8.0_Full_Source
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最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版
Part-E1-SDIO-Specification-Ver4.00-Final-120220.pdf：最新的sdio 4.0 spec 协议标准 带书签 高清完整PDF版，包括SDIO 接口的外设，SDIO 接口官方协议等。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
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