# Distance-Based Sound Separation

arXiv: https://arxiv.org/abs/2207.00562 · HTML: https://mlsanity.com/paper/2207.00562/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2022.

## Built on by (most cited first, 8 of 8)

- [ReZero: Region-customizable Sound Extraction](https://mlsanity.com/paper/2308.16892/) (2023)
- [Chat2Map: Efficient Scene Mapping from Multi-Ego Conversations](https://mlsanity.com/paper/2301.02184/) (2023)
- [Semantic Grouping Network for Audio Source Separation](https://mlsanity.com/paper/2407.03736/) (2024)
- [Binaural Angular Separation Network](https://mlsanity.com/paper/2401.08864/) (2024)
- [Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments](https://mlsanity.com/paper/2501.03045/) (2025)
- [Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids](https://mlsanity.com/paper/2603.02724/) (2026)
- [Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation](https://mlsanity.com/paper/2605.07694/) (2026)
- [Physics-Aware Deepfake Detection via Distance-Speech Consistency](https://mlsanity.com/paper/2610.18485/) (2026)

## Abstract (arXiv)

We propose the novel task of distance-based sound separation, where sounds are separated based only on their distance from a single microphone. In the context of assisted listening devices, proximity provides a simple criterion for sound selection in noisy environments that would allow the user to focus on sounds relevant to a local conversation. We demonstrate the feasibility of this approach by training a neural network to separate near sounds from far sounds in single channel synthetic reverberant mixtures, relative to a threshold distance defining the boundary between near and far. With a single nearby speaker and four distant speakers, the model improves scale-invariant signal to noise ratio by 4.4 dB for near sounds and 6.8 dB for far sounds.