# FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization

arXiv: https://arxiv.org/abs/2305.19610 · HTML: https://mlsanity.com/paper/2305.19610/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 10 of 10)

- [BAT: Learning to Reason about Spatial Sounds with Large Language Models](https://mlsanity.com/paper/2402.01591/) (2024)
- [SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models](https://mlsanity.com/paper/2511.06606/) (2025)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [Spatial Audio Processing with Large Language Model on Wearable Devices](https://mlsanity.com/paper/2504.08907/) (2025)
- [MFF-EINV2: Multi-scale Feature Fusion across Spectral-Spatial-Temporal Domains for Sound Event Localization and Detection](https://mlsanity.com/paper/2406.08771/) (2024)
- [wav2pos: Sound Source Localization using Masked Autoencoders](https://mlsanity.com/paper/2408.15771/) (2024)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio](https://mlsanity.com/paper/2411.13179/) (2024)
- [Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization](https://mlsanity.com/paper/2505.20961/) (2025)

## Abstract (arXiv)

Extracting direct-path spatial features is critical for sound source localization in adverse acoustic environments. This paper proposes a full-band and narrow-band fusion network for estimating direct-path inter-channel phase difference (DP-IPD) from microphone signals. The alternating full-band and narrow-band layers are responsible for learning the full-band correlation and narrow-band extraction of DP-IPD, respectively. Experiments show that the proposed network noticeably outperforms other advanced methods on both simulated and real-world data.