# Focus on the Sound around You: Monaural Target Speaker Extraction via Distance and Speaker Information

arXiv: https://arxiv.org/abs/2306.16241 · HTML: https://mlsanity.com/paper/2306.16241/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [ReZero: Region-customizable Sound Extraction](https://mlsanity.com/paper/2308.16892/) (2023)
- [Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments](https://mlsanity.com/paper/2501.03045/) (2025)
- [Robust Target Speaker Diarization and Separation via Augmented Speaker Embedding Sampling](https://mlsanity.com/paper/2508.06393/) (2025)

## Abstract (arXiv)

Previously, Target Speaker Extraction (TSE) has yielded outstanding performance in certain application scenarios for speech enhancement and source separation. However, obtaining auxiliary speaker-related information is still challenging in noisy environments with significant reverberation. inspired by the recently proposed distance-based sound separation, we propose the near sound (NS) extractor, which leverages distance information for TSE to reliably extract speaker information without requiring previous speaker enrolment, called speaker embedding self-enrollment (SESE). Full- & sub-band modeling is introduced to enhance our NS-Extractor's adaptability towards environments with significant reverberation. Experimental results on several cross-datasets demonstrate the effectiveness of our improvements and the excellent performance of our proposed NS-Extractor in different application scenarios.