# Directivity-Conditioned Low-Latency Neural Filtering for Speech Enhancement in Hearing Aids

arXiv: https://arxiv.org/abs/2609.15760 · HTML: https://mlsanity.com/paper/2609.15760/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 15 of 15)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation](https://mlsanity.com/paper/2307.16516/) (2023)
- [Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers](https://mlsanity.com/paper/2403.07675/) (2024)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency](https://mlsanity.com/paper/2204.09911/) (2022)
- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [ReZero: Region-customizable Sound Extraction](https://mlsanity.com/paper/2308.16892/) (2023)
- [Multichannel Speech Enhancement without Beamforming](https://mlsanity.com/paper/2110.13130/) (2021)
- [Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios](https://mlsanity.com/paper/2405.01967/) (2024)
- [Real-Time Streamable Generative Speech Restoration with Flow Matching](https://mlsanity.com/paper/2512.19442/) (2025)
- [Ultra-Low Latency Speech Enhancement - A Comprehensive Study](https://mlsanity.com/paper/2409.10358/) (2024)
- [Neural Directional Filtering: Far-Field Directivity Control With a Small Microphone Array](https://mlsanity.com/paper/2409.13502/) (2024)
- [Neural Directional Filtering with Configurable Directivity Pattern at Inference](https://mlsanity.com/paper/2510.20253/) (2025)

## Abstract (arXiv)

Latest advances in neural directional filtering show exceptional results in adapting the direction and shape of directivity patterns during the inference phase. However, in the existing methods for adapting directivity patterns during inference, important real-world constraints have been disregarded. Particularly for hearing devices, scenarios are often much more dynamic, microphone positions vary with head diameter and hearing aid placement, head-shadow effects occur, and strict latency constraints apply. In this work, we propose a novel low-latency (10 ms) deep neural network (DNN) taking the above requirements of hearing devices into account. As in recent work, we use feature-wise linear modulation (FiLM) to steer the directivity patterns during testing. To preserve the desired directivity pattern, a loss function is proposed that maintains the spectral cross-channel relationships. Interestingly, we are able to achieve similar results to methods with relaxed latency constraints.