# How to (virtually) train your speaker localizer

arXiv: https://arxiv.org/abs/2211.16958 · HTML: https://mlsanity.com/paper/2211.16958/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms](https://mlsanity.com/paper/2401.12238/) (2024)
- [Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement](https://mlsanity.com/paper/2510.23141/) (2025)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement](https://mlsanity.com/paper/2608.20971/) (2026)

## Abstract (arXiv)

Learning-based methods have become ubiquitous in speaker localization. Existing systems rely on simulated training sets for the lack of sufficiently large, diverse and annotated real datasets. Most room acoustics simulators used for this purpose rely on the image source method (ISM) because of its computational efficiency. This paper argues that carefully extending the ISM to incorporate more realistic surface, source and microphone responses into training sets can significantly boost the real-world performance of speaker localization systems. It is shown that increasing the training-set realism of a state-of-the-art direction-of-arrival estimator yields consistent improvements across three different real test sets featuring human speakers in a variety of rooms and various microphone arrays. An ablation study further reveals that every added layer of realism contributes positively to these improvements.