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Table of Contents
1. Introduction
2. Wavenet
2.1 Dilated Causal Convolutions
2.2 Softmax Distributions
2.3 Gated Activation Units
2.4 Residual and Skip Connections
2.5 Conditional Wavenets
2.6 Context Stacks
3. Experiments
3.1 Multi-Speaker Speech Generation
3.2 Text-to-Speech
3.3 Music
Summary
Wavenet is a deep neural network model introduced for generating raw audio waveforms. It is fully probabilistic and autoregressive, capable of efficiently training on data with high temporal resolution. The model has shown state-of-the-art performance in text-to-speech applications, generating natural-sounding speech. Wavenet can capture multiple speaker characteristics and generate music fragments realistically. The use of dilated causal convolutions allows for large receptive fields without increasing computational cost. The model employs softmax distributions and gated activation units for audio sample modeling. Conditional Wavenets can generate audio based on additional input variables. Context stacks are used to process long audio segments efficiently. Experimental results demonstrate Wavenet's effectiveness in multi-speaker speech generation, text-to-speech tasks, and music modeling.