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在 Android 应用程序中，我们经常需要播放各种音频文件。如果你需要在 Android 上播放 CAF 格式的音频文件，可以使用 AudioTrack 类来实现。本文将为你提供一个详细的指南，展示如何使用 Android 的 AudioTrack 类来播放 CAF 音频文件。
步骤 1: 导入音频文件
首先，将你的 CAF 格式音频文件添加到 Android 项目的资源目录中。你可以将音频文件放置在 “res/raw” 目录下。如果该目录不存在，则需要手动创建它。
步骤 2: 创建 AudioTrack 对象
在播放 CAF 音频之前，需要创建一个 AudioTrack 对象。可以通过指定一些参数来配置 AudioTrack 对象，例如音频流类型、采样率、声道配置和音频格式等。下面是一个示例代码片段，展示如何创建一个基本的 AudioTrack 对象：
AudioTrack audioTrack = new AudioTrack(
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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. 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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; 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