# FlowSep: Language-Queried Sound Separation with Rectified Flow Matching

arXiv: https://arxiv.org/abs/2409.07614 · HTML: https://mlsanity.com/paper/2409.07614/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 29 of its 35 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 29 of 29)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [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)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://mlsanity.com/paper/2303.17395/) (2023)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [Tango 2: Aligning Diffusion-based Text-to-Audio Generations through Direct Preference Optimization](https://mlsanity.com/paper/2404.09956/) (2024)
- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation](https://mlsanity.com/paper/2109.05418/) (2021)
- [Separate What You Describe: Language-Queried Audio Source Separation](https://mlsanity.com/paper/2203.15147/) (2022)
- [Separate Anything You Describe](https://mlsanity.com/paper/2308.05037/) (2023)
- [Listen to What You Want: Neural Network-based Universal Sound Selector](https://mlsanity.com/paper/2006.05712/) (2020)
- [Retrieval-Augmented Text-to-Audio Generation](https://mlsanity.com/paper/2309.08051/) (2023)
- [WavCraft: Audio Editing and Generation with Large Language Models](https://mlsanity.com/paper/2403.09527/) (2024)
- [Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music](https://mlsanity.com/paper/2008.05216/) (2020)
- [DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction](https://mlsanity.com/paper/2310.04567/) (2023)
- [Improving Target Sound Extraction with Timestamp Information](https://mlsanity.com/paper/2204.00821/) (2022)
- [Audio Prompt Tuning for Universal Sound Separation](https://mlsanity.com/paper/2311.18399/) (2023)
- [A Reference-free Metric for Language-Queried Audio Source Separation using Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2407.04936/) (2024)

## Built on by (most cited first, 21 of 21)

- [FlowSE: Efficient and High-Quality Speech Enhancement via Flow Matching](https://mlsanity.com/paper/2505.19476/) (2025)
- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [Flow Straight and Fast in Hilbert Space: Functional Rectified Flow](https://mlsanity.com/paper/2509.10384/) (2025)
- [UniFlow-Audio: Unified Flow Matching for Audio Generation from Omni-Modalities](https://mlsanity.com/paper/2509.24391/) (2025)
- [ZeroSep: Separate Anything in Audio with Zero Training](https://mlsanity.com/paper/2505.23625/) (2025)
- [UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling](https://mlsanity.com/paper/2508.07558/) (2025)
- [MMAudioSep: Taming Video-to-Audio Generative Model Towards Video/Text-Queried Sound Separation](https://mlsanity.com/paper/2510.09065/) (2025)
- [DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization](https://mlsanity.com/paper/2506.02858/) (2025)
- [DreamAudio: Customized Text-to-Audio Generation with Diffusion Models](https://mlsanity.com/paper/2509.06027/) (2025)
- [Training-Free Multi-Step Audio Source Separation](https://mlsanity.com/paper/2505.19534/) (2025)
- [Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation](https://mlsanity.com/paper/2506.08457/) (2025)
- [MARS-Sep: Multimodal-Aligned Reinforced Sound Separation](https://mlsanity.com/paper/2510.10509/) (2025)
- [ImmersiveFlow: Stereo-to-7.1.4 spatial audio generation with flow matching](https://mlsanity.com/paper/2601.12950/) (2026)
- [SAM Audio Judge: A Unified Multimodal Framework for Perceptual Evaluation of Audio Separation](https://mlsanity.com/paper/2601.19702/) (2026)
- [FlowSep 2: Self-Supervised Flow Matching for Language-Queried Audio Source Separation](https://mlsanity.com/paper/2608.22111/) (2026)
- [Text-Queried Audio Source Separation via Hierarchical Modeling](https://mlsanity.com/paper/2505.21025/) (2025)
- [Latent-Level Enhancement with Flow Matching for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2601.04459/) (2026)
- [Fine-grained Soundscape Control for Augmented Hearing](https://mlsanity.com/paper/2603.00395/) (2026)
- [DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers](https://mlsanity.com/paper/2603.21608/) (2026)
- [Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models](https://mlsanity.com/paper/2606.10046/) (2026)
- [Iterative Audio Separation with Mixture Consistency via MIMO Model Extension](https://mlsanity.com/paper/2609.07226/) (2026)

## Abstract (arXiv)

Language-queried audio source separation (LASS) focuses on separating sounds using textual descriptions of the desired sources. Current methods mainly use discriminative approaches, such as time-frequency masking, to separate target sounds and minimize interference from other sources. However, these models face challenges when separating overlapping soundtracks, which may lead to artifacts such as spectral holes or incomplete separation. Rectified flow matching (RFM), a generative model that establishes linear relations between the distribution of data and noise, offers superior theoretical properties and simplicity, but has not yet been explored in sound separation. In this work, we introduce FlowSep, a new generative model based on RFM for LASS tasks. FlowSep learns linear flow trajectories from noise to target source features within the variational autoencoder (VAE) latent space. During inference, the RFM-generated latent features are reconstructed into a mel-spectrogram via the pre-trained VAE decoder, followed by a pre-trained vocoder to synthesize the waveform. Trained on 1,680 hours of audio data, FlowSep outperforms the state-of-the-art models across multiple benchmarks, as evaluated with subjective and objective metrics. Additionally, our results show that FlowSep surpasses a diffusion-based LASS model in both separation quality and inference efficiency, highlighting its strong potential for audio source separation tasks. Code, pre-trained models and demos can be found at: https://audio-agi.github.io/FlowSep_demo/ .