SING: Symbol-to-Instrument Neural Generator

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz... (read more)

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Methods used in the Paper


METHOD TYPE
Mixture of Logistic Distributions
Output Functions
Dilated Causal Convolution
Temporal Convolutions
WaveNet
Generative Audio Models