Introduction to WaveRNN
WaveRNN is a type of neural network that is used for generating audio. This network is designed to predict 16-bit raw audio samples with high efficiency. It is a single-layer recurrent neural network that consists of different computations, including sigmoid and tanh non-linearities, matrix-vector products, and softmax layers.
How WaveRNN Works
WaveRNN works by predicting audio samples from coarse and fine parts that are encoded as scalars in a range of 0 to 255. These parts are then scaled to the interval of -1 to 1. WaveRNN uses a masked matrix to connect the last coarse input to the fine part of the states. This fine part is what affects the fine output. WaveRNN consists of different computations:
Concatenation Operation
The first operation in WaveRNN that concatenates different inputs. Using the recurrence equation:
$$ \mathbf{x}\_{t} = \left[\mathbf{c}\_{t−1},\mathbf{f}\_{t−1}, \mathbf{c}\_{t}\right] $$
The inputs concatenated together are:
- The previous coarse input
- The previous fine input
- The current coarse input
Gates
The next operation is Gates. These are used to modify the information that flows to the next hidden state. They allow the network to remember previous information and forget it after a while. Two types of gates used are:
- Update gate ($\mathbf{u}\_{t}$) – decides how much of the previous hidden state is retained and how much of the new information is used to compute the new hidden state.
- Reset gate ($\mathbf{r}\_{t}$) – decides how much of the new input is used to update the current hidden state.
State Activation
The next operation is the state activation that decides the new hidden state:
$$ \mathbf{h}\_{t} = \mathbf{u}\_{t} \cdot \mathbf{h}\_{t-1} + \left(1-\mathbf{u}\_{t}\right) \cdot \mathbf{e}\_{t} $$
Where:
- $\mathbf{u}\_{t}$ is the update gate output
- $1-\mathbf{u}\_{t}$ is the amount of new information
- $\mathbf{e}\_{t}$ is the input after resetting
Output
The last operation is output, which is used to generate audio. The Dual-SOftmax output layer allows efficient prediction of 16-bit samples using only two small output spaces that consist of 2 8 values each, instead of a single large output space.
Applications of WaveRNN
WaveRNN has a wide range of applications in various fields like digital audio signal processing, music synthesis, speech recognition, and natural language processing. This neural network technology can generate human-like, high-quality audio that can be easily integrated into machines, reducing human efforts and saving time.
Digital Audio Signal Processing
WaveRNN is used in digital audio signal processing to analyze and manipulate digital audio signals. It can be useful in speech processing, voice recognition, noise filtering, and much more.
Music Synthesis
WaveRNN is capable of making music synthesis easier and more efficient. With this technology, music producers can create complex instruments using deep-learning methods.
Speech Recognition
WaveRNN can be used in speech recognition to identify and separate voices from the background noise. It can help to transform audio signals into text and identify certain sounds used in speech.
Natural Language Processing
WaveRNN is used in Natural Language Processing (NLP) to improve text-to-speech systems. It can be used to synthesize human-like voices, improving user experience, and accessibility.
WaveRNN is a powerful neural network technology for audio generation, and its applications go beyond just that. It can be used in different fields to simplify and automate various tasks. Its use in music synthesis, speech recognition, and natural language processing has shown how it can make work more efficient while reducing human efforts.
