# Neural Directed Speech Enhancement with Dual Microphone Array in High Noise Scenario

arXiv: https://arxiv.org/abs/2412.18141 · HTML: https://mlsanity.com/paper/2412.18141/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## 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, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech](https://mlsanity.com/paper/2110.05866/) (2021)
- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [Distance-Based Sound Separation](https://mlsanity.com/paper/2207.00562/) (2022)
- [HGCN: Harmonic gated compensation network for speech enhancement](https://mlsanity.com/paper/2201.12755/) (2022)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [VSEGAN: Visual Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/2102.02599/) (2021)
- [Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation in Complex Domain](https://mlsanity.com/paper/2104.12359/) (2021)
- [Learning to Separate Voices by Spatial Regions](https://mlsanity.com/paper/2207.04203/) (2022)
- [Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech](https://mlsanity.com/paper/2401.06485/) (2024)
- [Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter](https://mlsanity.com/paper/2407.04219/) (2024)
- [Time-domain Speech Enhancement Assisted by Multi-resolution Frequency Encoder and Decoder](https://mlsanity.com/paper/2303.14593/) (2023)
- [Text-aware Speech Separation for Multi-talker Keyword Spotting](https://mlsanity.com/paper/2406.12447/) (2024)
- [NTC-KWS: Noise-aware CTC for Robust Keyword Spotting](https://mlsanity.com/paper/2412.12614/) (2024)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [TDT-KWS: Fast And Accurate Keyword Spotting Using Token-and-duration Transducer](https://mlsanity.com/paper/2403.13332/) (2024)
- [Two Channel Audio Zooming System For Smartphone](https://mlsanity.com/paper/2001.04940/) (2020)
- [Binaural Angular Separation Network](https://mlsanity.com/paper/2401.08864/) (2024)
- [EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement](https://mlsanity.com/paper/2202.06507/) (2022)
- [Streaming Keyword Spotting Boosted by Cross-layer Discrimination Consistency](https://mlsanity.com/paper/2412.12635/) (2024)

## Built on by (most cited first, 2 of 2)

- [End-to-End DOA-Guided Speech Extraction in Noisy Multi-Talker Scenarios](https://mlsanity.com/paper/2507.20926/) (2025)
- [Spatial Speech Perception Systems: A Survey of Sound Source Localization, Directional Enhancement, and Speech Recognition](https://mlsanity.com/paper/2607.02296/) (2026)

## Abstract (arXiv)

In multi-speaker scenarios, leveraging spatial features is essential for enhancing target speech. While with limited microphone arrays, developing a compact multi-channel speech enhancement system remains challenging, especially in extremely low signal-to-noise ratio (SNR) conditions. To tackle this issue, we propose a triple-steering spatial selection method, a flexible framework that uses three steering vectors to guide enhancement and determine the enhancement range. Specifically, we introduce a causal-directed U-Net (CDUNet) model, which takes raw multi-channel speech and the desired enhancement width as inputs. This enables dynamic adjustment of steering vectors based on the target direction and fine-tuning of the enhancement region according to the angular separation between the target and interference signals. Our model with only a dual microphone array, excels in both speech quality and downstream task performance. It operates in real-time with minimal parameters, making it ideal for low-latency, on-device streaming applications.