Audio Generation
113 papers with code • 3 benchmarks • 10 datasets
Audio generation (synthesis) is the task of generating raw audio such as speech.
( Image credit: MelNet )
Benchmarks
These leaderboards are used to track progress in Audio Generation
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Libraries
Use these libraries to find Audio Generation models and implementationsDatasets
AudioCaps
NSynth
MAESTRO
MovieNet
SymphonyNet
NES-MDB
Audio-alpaca
Most implemented papers
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
Adversarial Audio Synthesis
chrisdonahue/wavegan
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ICLR 2019
Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.
GANSynth: Adversarial Neural Audio Synthesis
tensorflow/magenta
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ICLR 2019
Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence.
It's Raw! Audio Generation with State-Space Models
SaShiMi yields state-of-the-art performance for unconditional waveform generation in the autoregressive setting.
MelNet: A Generative Model for Audio in the Frequency Domain
fatchord/MelNet
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Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps.
BigVGAN: A Universal Neural Vocoder with Large-Scale Training
nvidia/bigvgan
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Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments.
AudioLM: a Language Modeling Approach to Audio Generation
suno-ai/bark
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We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
soroushmehr/sampleRNN_ICLR2017
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In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
Audio Super Resolution using Neural Networks
We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks.
HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
andreevp/wvmos
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Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.
