Audio Generation
64 papers with code • 3 benchmarks • 8 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
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AudioCaps
NSynth
MAESTRO
MovieNet
SymphonyNet
NES-MDB
Latest papers with no code
EVA-GAN: Enhanced Various Audio Generation via Scalable Generative Adversarial Networks
The advent of Large Models marks a new era in machine learning, significantly outperforming smaller models by leveraging vast datasets to capture and synthesize complex patterns.
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering
The language model (LM) approach based on acoustic and linguistic prompts, such as VALL-E, has achieved remarkable progress in the field of zero-shot audio generation.
Masked Audio Generation using a Single Non-Autoregressive Transformer
We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens.
Efficient Parallel Audio Generation using Group Masked Language Modeling
We present a fast and high-quality codec language model for parallel audio generation.
Audiobox: Unified Audio Generation with Natural Language Prompts
Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data.
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models
Denoising Diffusion Probabilistic Model (DDPM) has shown great competence in image and audio generation tasks.
CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling
We introduce a multi-modal diffusion model tailored for the bi-directional conditional generation of video and audio.
SEFGAN: Harvesting the Power of Normalizing Flows and GANs for Efficient High-Quality Speech Enhancement
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE).
tinyCLAP: Distilling Constrastive Language-Audio Pretrained Models
Contrastive Language-Audio Pretraining (CLAP) became of crucial importance in the field of audio and speech processing.
Cross-modal Generative Model for Visual-Guided Binaural Stereo Generation
To this end, a metric to measure the spatial perception of audio is proposed for the first time.
