# Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation

arXiv: https://arxiv.org/abs/2212.01012 · HTML: https://mlsanity.com/paper/2212.01012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speech and audio distillation, one of 12 directions in Knowledge distillation.
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Placed in

- Knowledge distillation / [Speech and audio distillation](https://mlsanity.com/distill/speech-and-audio-distillation/)

## Builds on (most cited first, 7 of 7)

- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [The Conversation: Deep Audio-Visual Speech Enhancement](https://mlsanity.com/paper/1804.04121/) (2018)
- [PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network](https://mlsanity.com/paper/1911.04697/) (2019)
- [Channel-Attention Dense U-Net for Multichannel Speech Enhancement](https://mlsanity.com/paper/2001.11542/) (2020)
- [Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks](https://mlsanity.com/paper/2102.12078/) (2021)
- [Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement](https://mlsanity.com/paper/2107.12601/) (2021)
- [Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention](https://mlsanity.com/paper/2205.01280/) (2022)

## Abstract (arXiv)

Monaural speech enhancement (SE) provides a versatile and cost-effective approach to SE tasks by utilizing recordings from a single microphone. However, the monaural SE lags performance behind multi-channel SE as the monaural SE methods are unable to extract spatial information from one-channel recordings, which greatly limits their application scenarios. To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions. Extensive experiments show that our proposed monaural SE model by injecting spatial information via knowledge distillation achieves favorable performance against other monaural SE models with fewer parameters.