# LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation

arXiv: https://arxiv.org/abs/2010.11631 · HTML: https://mlsanity.com/paper/2010.11631/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [Multi-Source Diffusion Models for Simultaneous Music Generation and Separation](https://mlsanity.com/paper/2302.02257/) (2023)
- [Simultaneous Music Separation and Generation Using Multi-Track Latent Diffusion Models](https://mlsanity.com/paper/2409.12346/) (2024)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [Audiovisual Singing Voice Separation](https://mlsanity.com/paper/2107.00231/) (2021)
- [AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries](https://mlsanity.com/paper/2104.13553/) (2021)
- [Unsupervised Source Separation via Bayesian Inference in the Latent Domain](https://mlsanity.com/paper/2110.05313/) (2021)
- [AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning](https://mlsanity.com/paper/2310.18320/) (2023)
- [Improving Music Source Separation with Diffusion and Consistency Refinement](https://mlsanity.com/paper/2412.06965/) (2024)
- [LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation](https://mlsanity.com/paper/2111.12516/) (2021)
- [Learning source-aware representations of music in a discrete latent space](https://mlsanity.com/paper/2111.13321/) (2021)
- [Resource-constrained stereo singing voice cancellation](https://mlsanity.com/paper/2401.12068/) (2024)
- [Source Separation of Multi-source Raw Music using a Residual Quantized Variational Autoencoder](https://mlsanity.com/paper/2408.07020/) (2024)
- [Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions](https://mlsanity.com/paper/2508.14556/) (2025)
- [A Conditioned UNet for Music Source Separation](https://mlsanity.com/paper/2512.15532/) (2025)

## Abstract (arXiv)

Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns. The goal of this paper is to extend the FT block to fit the multi-source task. We propose the Latent Source Attentive Frequency Transformation (LaSAFT) block to capture source-dependent frequency patterns. We also propose the Gated Point-wise Convolutional Modulation (GPoCM), an extension of Feature-wise Linear Modulation (FiLM), to modulate internal features. By employing these two novel methods, we extend the Conditioned-U-Net (CUNet) for multi-source separation, and the experimental results indicate that our LaSAFT and GPoCM can improve the CUNet's performance, achieving state-of-the-art SDR performance on several MUSDB18 source separation tasks.