# Reference Microphone Selection for Guided Source Separation based on the Normalized L-p Norm

arXiv: https://arxiv.org/abs/2510.27198 · HTML: https://mlsanity.com/paper/2510.27198/

## 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 10 of its 23 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## 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, 10 of 10)

- [DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors](https://mlsanity.com/paper/2110.01763/) (2021)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio](https://mlsanity.com/paper/2304.01448/) (2023)
- [SCOREQ: Speech Quality Assessment with Contrastive Regression](https://mlsanity.com/paper/2410.06675/) (2024)
- [VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music](https://mlsanity.com/paper/2412.17667/) (2024)
- [The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization](https://mlsanity.com/paper/2407.16447/) (2024)
- [STCON System for the CHiME-8 Challenge](https://mlsanity.com/paper/2410.13411/) (2024)
- [The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System](https://mlsanity.com/paper/2310.12378/) (2023)
- [Learning to Rank Microphones for Distant Speech Recognition](https://mlsanity.com/paper/2104.02819/) (2021)
- [Reference Microphone Selection for the Weighted Prediction Error Algorithm using the Normalized L-p Norm](https://mlsanity.com/paper/2411.03168/) (2024)

## Abstract (arXiv)

Guided Source Separation (GSS) is a popular front-end for distant automatic speech recognition (ASR) systems using spatially distributed microphones. When considering spatially distributed microphones, the choice of reference microphone may have a large influence on the quality of the output signal and the downstream ASR performance. In GSS-based speech enhancement, reference microphone selection is typically performed using the signal-to-noise ratio (SNR), which is optimal for noise reduction but may neglect differences in early-to-late-reverberant ratio (ELR) across microphones. In this paper, we propose two reference microphone selection methods for GSS-based speech enhancement that are based on the normalized ℓp-norm, either using only the normalized ℓp-norm or combining the normalized ℓp-norm and the SNR to account for both differences in SNR and ELR across microphones. Experimental evaluation using a CHiME-8 distant ASR system shows that the proposed ℓp-norm-based methods outperform the baseline method, reducing the macro-average word error rate.