# Separate Anything You Describe

arXiv: https://arxiv.org/abs/2308.05037 · HTML: https://mlsanity.com/paper/2308.05037/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 101 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 30 of 73)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Clotho: An Audio Captioning Dataset](https://mlsanity.com/paper/1910.09387/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [VisualVoice: Audio-Visual Speech Separation with Cross-Modal Consistency](https://mlsanity.com/paper/2101.03149/) (2021)
- [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019)
- [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023)
- [Audio Retrieval with Natural Language Queries: A Benchmark Study](https://mlsanity.com/paper/2112.09418/) (2021)

## Built on by (most cited first, 30 of 43)

- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization](https://mlsanity.com/paper/2503.23377/) (2025)
- [Taming Data and Transformers for Audio Generation](https://mlsanity.com/paper/2406.19388/) (2024)
- [FlowSep: Language-Queried Sound Separation with Rectified Flow Matching](https://mlsanity.com/paper/2409.07614/) (2024)
- [Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labeling](https://mlsanity.com/paper/2406.00919/) (2024)
- [VinTAGe: Joint Video and Text Conditioning for Holistic Audio Generation](https://mlsanity.com/paper/2412.10768/) (2024)
- [Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning](https://mlsanity.com/paper/2405.18386/) (2024)
- [Guiding Audio Editing with Audio Language Model](https://mlsanity.com/paper/2509.21625/) (2025)
- [SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer](https://mlsanity.com/paper/2409.08425/) (2024)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [GASS: Generalizing Audio Source Separation with Large-scale Data](https://mlsanity.com/paper/2310.00140/) (2023)
- [Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction](https://mlsanity.com/paper/2310.07284/) (2023)
- [Listen, Chat, and Remix: Text-Guided Soundscape Remixing for Enhanced Auditory Experience](https://mlsanity.com/paper/2402.03710/) (2024)
- [Learning Source Disentanglement in Neural Audio Codec](https://mlsanity.com/paper/2409.11228/) (2024)
- [Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond](https://mlsanity.com/paper/2505.04621/) (2025)
- [Weakly-supervised Audio Separation via Bi-modal Semantic Similarity](https://mlsanity.com/paper/2404.01740/) (2024)
- [Target conversation extraction: Source separation using turn-taking dynamics](https://mlsanity.com/paper/2407.11277/) (2024)
- [MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction](https://mlsanity.com/paper/2505.23305/) (2025)
- [ZeroSep: Separate Anything in Audio with Zero Training](https://mlsanity.com/paper/2505.23625/) (2025)
- [FlexSED: Towards Open-Vocabulary Sound Event Detection](https://mlsanity.com/paper/2509.18606/) (2025)
- [Event2Audio: Event-Based Optical Vibration Sensing](https://mlsanity.com/paper/2507.03273/) (2025)
- [MMAudioSep: Taming Video-to-Audio Generative Model Towards Video/Text-Queried Sound Separation](https://mlsanity.com/paper/2510.09065/) (2025)
- [Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues](https://mlsanity.com/paper/2409.12415/) (2024)
- [AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting](https://mlsanity.com/paper/2506.01015/) (2025)
- [DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization](https://mlsanity.com/paper/2506.02858/) (2025)
- [Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance](https://mlsanity.com/paper/2506.20995/) (2025)
- [Neural Speech Extraction with Human Feedback](https://mlsanity.com/paper/2508.03041/) (2025)
- [Universal Sound Separation with Self-Supervised Audio Masked Autoencoder](https://mlsanity.com/paper/2407.11745/) (2024)
- [Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs](https://mlsanity.com/paper/2408.09269/) (2024)
- [EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation](https://mlsanity.com/paper/2410.12028/) (2024)

## Abstract (arXiv)

Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA). LASS aims to separate a target sound from an audio mixture given a natural language query, which provides a natural and scalable interface for digital audio applications. Recent works on LASS, despite attaining promising separation performance on specific sources (e.g., musical instruments, limited classes of audio events), are unable to separate audio concepts in the open domain. In this work, we introduce AudioSep, a foundation model for open-domain audio source separation with natural language queries. We train AudioSep on large-scale multimodal datasets and extensively evaluate its capabilities on numerous tasks including audio event separation, musical instrument separation, and speech enhancement. AudioSep demonstrates strong separation performance and impressive zero-shot generalization ability using audio captions or text labels as queries, substantially outperforming previous audio-queried and language-queried sound separation models. For reproducibility of this work, we will release the source code, evaluation benchmark and pre-trained model at: https://github.com/Audio-AGI/AudioSep.