Audio generation (synthesis) is the task of generating raw audio such as speech.
( Image credit: MelNet )
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Hence, with the extensive experimental results, we have demonstrated that by harnessing the power of the high-fidelity audio generation, the proposed GAAE model can learn powerful representation from unlabelled dataset leveraging a fewer percentage of labelled data as supervision/guidance.
However, the model can become redundant if it is intended for a specific task.
Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain.
While WaveNet produces state-of-the art audio generation results, the naive inference implementation is quite slow; it takes a few minutes to generate just one second of audio on a high-end GPU.
This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform.
Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different methods of waveform generation.