# Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence

arXiv: https://arxiv.org/abs/2211.08748 · HTML: https://mlsanity.com/paper/2211.08748/

## 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 26 of its 46 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## 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, 26 of 26)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [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)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation](https://mlsanity.com/paper/2008.09586/) (2020)
- [DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering](https://mlsanity.com/paper/2110.05588/) (2021)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [SpEx+: A Complete Time Domain Speaker Extraction Network](https://mlsanity.com/paper/2005.04686/) (2020)
- [Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam](https://mlsanity.com/paper/2001.08378/) (2020)
- [Deep attractor network for single-microphone speaker separation](https://mlsanity.com/paper/1611.08930/) (2016)
- [Personalized Speech Enhancement: New Models and Comprehensive Evaluation](https://mlsanity.com/paper/2110.09625/) (2021)
- [Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement](https://mlsanity.com/paper/2106.04129/) (2021)
- [Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation](https://mlsanity.com/paper/2204.00771/) (2022)
- [Atss-Net: Target Speaker Separation via Attention-based Neural Network](https://mlsanity.com/paper/2005.09200/) (2020)
- [X-TaSNet: Robust and Accurate Time-Domain Speaker Extraction Network](https://mlsanity.com/paper/2010.12766/) (2020)
- [One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement](https://mlsanity.com/paper/2110.10330/) (2021)
- [Speaker activity driven neural speech extraction](https://mlsanity.com/paper/2101.05516/) (2021)
- [Improving Channel Decorrelation for Multi-Channel Target Speech Extraction](https://mlsanity.com/paper/2106.03113/) (2021)
- [Learning-based personal speech enhancement for teleconferencing by exploiting spatial-spectral features](https://mlsanity.com/paper/2112.05686/) (2021)

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

- [Spatial-Temporal Activity-Informed Diarization and Separation](https://mlsanity.com/paper/2401.16850/) (2024)
- [HyBeam: Hybrid Microphone-Beamforming Array-Agnostic Speech Enhancement for Wearables](https://mlsanity.com/paper/2510.22637/) (2025)

## Abstract (arXiv)

Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues. Compared with single channel approaches, multichannel PSE systems can be more effective in adverse acoustic conditions by leveraging the spatial information in microphone signals. However, the implementation of multichannel PSEs to accommodate a wide range of array topology in household applications can be challenging. To develop an array configuration agnostic PSE system, we define a spatial feature termed the long short term spatial coherence as the input feature to a convolutional recurrent network to monitor the voice activity of the target speaker. As another refinement, an equivalent rectangular bandwidth scaled LSTSC feature can be used to reduce the computational cost. Experiments were conducted to compare the proposed PSE systems, including the complete and the simplified versions with two baselines using unseen room responses and array configurations in the presence of TV noise and competing speakers. The results demonstrated that the proposed multichannel PSE network trained with the LSTSC feature achieved superior enhancement performance without precise knowledge of the array configurations and room responses.