# Full-band General Audio Synthesis with Score-based Diffusion

arXiv: https://arxiv.org/abs/2210.14661 · HTML: https://mlsanity.com/paper/2210.14661/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [PixelSNAIL: An Improved Autoregressive Generative Model](https://mlsanity.com/paper/1712.09763/) (2017)
- [Adversarial score matching and improved sampling for image generation](https://mlsanity.com/paper/2009.05475/) (2020)
- [Universal Speech Enhancement with Score-based Diffusion](https://mlsanity.com/paper/2206.03065/) (2022)
- [CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis](https://mlsanity.com/paper/2106.07431/) (2021)
- [DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks](https://mlsanity.com/paper/2008.12073/) (2020)
- [Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning](https://mlsanity.com/paper/2107.09998/) (2021)
- [Generating Diverse Realistic Laughter for Interactive Art](https://mlsanity.com/paper/2111.03146/) (2021)
- [On tuning consistent annealed sampling for denoising score matching](https://mlsanity.com/paper/2104.03725/) (2021)
- [Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks](https://mlsanity.com/paper/2110.09605/) (2021)

## Built on by (most cited first, 30 of 32)

- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [Long-form music generation with latent diffusion](https://mlsanity.com/paper/2404.10301/) (2024)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
- [Multi-Source Diffusion Models for Simultaneous Music Generation and Separation](https://mlsanity.com/paper/2302.02257/) (2023)
- [Scaling Transformers for Low-Bitrate High-Quality Speech Coding](https://mlsanity.com/paper/2411.19842/) (2024)
- [Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity](https://mlsanity.com/paper/2407.10387/) (2024)
- [SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis](https://mlsanity.com/paper/2310.15247/) (2023)
- [From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion](https://mlsanity.com/paper/2308.02560/) (2023)
- [Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach](https://mlsanity.com/paper/2306.01433/) (2023)
- [CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models](https://mlsanity.com/paper/2306.09635/) (2023)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [Audio Generation with Multiple Conditional Diffusion Model](https://mlsanity.com/paper/2308.11940/) (2023)
- [EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis](https://mlsanity.com/paper/2311.08667/) (2023)
- [Image Synthesis under Limited Data: A Survey and Taxonomy](https://mlsanity.com/paper/2307.16879/) (2023)
- [Generative De-Quantization for Neural Speech Codec via Latent Diffusion](https://mlsanity.com/paper/2311.08330/) (2023)
- [Naturalistic Music Decoding from EEG Data via Latent Diffusion Models](https://mlsanity.com/paper/2405.09062/) (2024)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Woosh: A Sound Effects Foundation Model](https://mlsanity.com/paper/2604.01929/) (2026)
- [Towards Flexible, Scalable, and Adaptive Multi-Modal Conditioned Face Synthesis](https://mlsanity.com/paper/2312.16274/) (2023)
- [Enhancing Emotional Text-to-Speech Controllability with Natural Language Guidance through Contrastive Learning and Diffusion Models](https://mlsanity.com/paper/2409.06451/) (2024)
- [MambaFoley: Foley Sound Generation using Selective State-Space Models](https://mlsanity.com/paper/2409.09162/) (2024)
- [AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design](https://mlsanity.com/paper/2404.02003/) (2024)
- [SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions](https://mlsanity.com/paper/2510.21330/) (2025)
- [Distribution Preserving Source Separation With Time Frequency Predictive Models](https://mlsanity.com/paper/2303.05896/) (2023)
- [Energy-Based Models For Speech Synthesis](https://mlsanity.com/paper/2310.12765/) (2023)
- [Audio Decoding by Inverse Problem Solving](https://mlsanity.com/paper/2409.07858/) (2024)
- [Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data](https://mlsanity.com/paper/2505.24556/) (2025)
- [Poisson Flow Consistency Training](https://mlsanity.com/paper/2510.21857/) (2025)

## Abstract (arXiv)

Recent works have shown the capability of deep generative models to tackle general audio synthesis from a single label, producing a variety of impulsive, tonal, and environmental sounds. Such models operate on band-limited signals and, as a result of an autoregressive approach, they are typically conformed by pre-trained latent encoders and/or several cascaded modules. In this work, we propose a diffusion-based generative model for general audio synthesis, named DAG, which deals with full-band signals end-to-end in the waveform domain. Results show the superiority of DAG over existing label-conditioned generators in terms of both quality and diversity. More specifically, when compared to the state of the art, the band-limited and full-band versions of DAG achieve relative improvements that go up to 40 and 65%, respectively. We believe DAG is flexible enough to accommodate different conditioning schemas while providing good quality synthesis.