Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments - Info and Reading Options

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Front Cover -- Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments -- Copyright -- Contents -- 1 Introduction of the book -- 2 Fundamentals of deep learning -- 2.1 Supervised learning -- 2.2 Single-layer neural network -- 2.2.1 Basic model -- 2.2.2 Activation functions -- 2.3 Feedforward deep neural network -- 2.3.1 Backpropagation algorithm -- 2.3.2 Regularization -- 2.4 Recurrent neural networks (RNN) -- 2.4.1 Fundamentals of recurrent neural networks -- 2.4.1.1 Basic structure -- 2.4.1.2 Backpropagation through time (BPTT) -- 2.4.1.3 Gradient vanishing and exploding in RNNs -- 2.4.2 Long short-term memory (LSTM) -- 2.4.3 Gated recurrent unit (GRU) -- 2.4.4 Deep RNN structures -- 2.4.5 RNN modeling framework for sequential data -- 2.5 Convolutional neural networks -- 2.5.1 Basics of convolutional neural networks -- 2.5.1.1 Convolutional operation -- 2.5.1.2 Pooling -- 2.5.1.3 Multichannel multigroup convolution -- 2.5.2 Other forms of convolution -- 2.5.2.1 Dilated convolution -- 2.5.2.2 Deconvolution -- 2.5.2.3 Depthwise separable convolution -- 2.5.3 Residual neural network -- 2.5.3.1 Residual unit -- 2.5.3.2 Network architecture design -- 2.5.4 Temporal convolutional network (TCN) -- 2.6 Normalization in neural networks -- 2.6.1 Batch normalization -- 2.6.2 Layer normalization -- 2.7 Attention mechanism in neural networks -- 2.7.1 Encoder-decoder framework -- 2.7.2 Encoder-attention-decoder framework -- 2.7.3 Monotonic attention mechanism -- 2.7.4 Transformer -- 2.8 Generative adversarial networks -- 2.8.1 Basic structure -- 2.8.2 Model training -- 2.9 Summary of this chapter -- 3 Voice activity detection -- 3.1 Introduction -- 3.2 Fundamental knowledge -- 3.2.1 Signal models -- 3.2.2 Evaluation metrics -- 3.3 Voice activity detection models -- 3.3.1 Basic framework of voice activity detection models

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