# Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers

arXiv: https://arxiv.org/abs/2603.23723 · HTML: https://mlsanity.com/paper/2603.23723/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 81 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 30 of 39)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [An Investigation of Incorporating Mamba for Speech Enhancement](https://mlsanity.com/paper/2405.06573/) (2024)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [Neural Target Speech Extraction: An Overview](https://mlsanity.com/paper/2301.13341/) (2023)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers](https://mlsanity.com/paper/2403.07675/) (2024)
- [The LOCATA Challenge: Acoustic Source Localization and Tracking](https://mlsanity.com/paper/1909.01008/) (2019)
- [SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation](https://mlsanity.com/paper/2307.16516/) (2023)
- [Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2006.09006/) (2020)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization](https://mlsanity.com/paper/2305.19610/) (2023)
- [ReZero: Region-customizable Sound Extraction](https://mlsanity.com/paper/2308.16892/) (2023)
- [All Neural Low-latency Directional Speech Extraction](https://mlsanity.com/paper/2407.04879/) (2024)
- [LocSelect: Target Speaker Localization with an Auditory Selective Hearing Mechanism](https://mlsanity.com/paper/2310.10497/) (2023)
- [SRP-DNN: Learning Direct-Path Phase Difference for Multiple Moving Sound Source Localization](https://mlsanity.com/paper/2202.07859/) (2022)
- [Iterative autoregression: a novel trick to improve your low-latency speech enhancement model](https://mlsanity.com/paper/2211.01751/) (2022)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [Listen only to me! How well can target speech extraction handle false alarms?](https://mlsanity.com/paper/2204.04811/) (2022)
- [Mask-based Neural Beamforming for Moving Speakers with Self-Attention-based Tracking](https://mlsanity.com/paper/2205.03568/) (2022)
- [Steering Deep Non-Linear Spatially Selective Filters for Weakly Guided Extraction of Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2505.14517/) (2025)
- [6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human](https://mlsanity.com/paper/2403.01670/) (2024)

## Built on by (most cited first, 1 of 1)

- [Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics](https://mlsanity.com/paper/2607.04471/) (2026)

## Abstract (arXiv)

Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known directions. To retain this level of performance in dynamic scenarios where only the speakers' initial directions are given, accurate, yet computationally lightweight tracking algorithms become necessary. Assuming a frame-wise causal processing style, temporal feedback allows for leveraging the enhanced speech signal to improve tracking performance. In this work, we investigate strategies to incorporate the enhanced signal into lightweight tracking algorithms and autoregressively guide deep spatial filters. Our proposed Bayesian tracking algorithms are compatible with arbitrary deep spatial filters. To increase the realism of simulated trajectories during development and evaluation, we develop a synthetic data generation framework based on the social force model. Results validate that the autoregressive incorporation significantly improves the accuracy of our Bayesian trackers, resulting in superior enhancement with none or only negligibly increased computational overhead. Real-world recordings complement these findings and demonstrate the generalizability of our methods to unseen acoustic conditions.