# End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions

arXiv: https://arxiv.org/abs/2502.06285 · HTML: https://mlsanity.com/paper/2502.06285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Target Speech Extraction: An Overview](https://mlsanity.com/paper/2301.13341/) (2023)
- [FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing](https://mlsanity.com/paper/1909.13387/) (2019)
- [Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam](https://mlsanity.com/paper/2001.08378/) (2020)
- [Multi-modal Multi-channel Target Speech Separation](https://mlsanity.com/paper/2003.07032/) (2020)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement](https://mlsanity.com/paper/2109.00265/) (2021)
- [Neural Spatio-Temporal Beamformer for Target Speech Separation](https://mlsanity.com/paper/2005.03889/) (2020)
- [L-SpEx: Localized Target Speaker Extraction](https://mlsanity.com/paper/2202.09995/) (2022)
- [Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain](https://mlsanity.com/paper/2206.15423/) (2022)
- [Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement](https://mlsanity.com/paper/2108.03130/) (2021)
- [Time-domain speaker extraction network](https://mlsanity.com/paper/2004.14762/) (2020)
- [Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction](https://mlsanity.com/paper/2210.15512/) (2022)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [NICE-Beam: Neural Integrated Covariance Estimators for Time-Varying Beamformers](https://mlsanity.com/paper/2112.04613/) (2021)
- [Multi-channel target speech extraction with channel decorrelation and target speaker adaptation](https://mlsanity.com/paper/2010.09191/) (2020)
- [A two-stage speaker extraction algorithm under adverse acoustic conditions using a single-microphone](https://mlsanity.com/paper/2303.07072/) (2023)
- [Beamformer-Guided Target Speaker Extraction](https://mlsanity.com/paper/2303.08702/) (2023)
- [Explainable DNN-based Beamformer with Postfilter](https://mlsanity.com/paper/2411.10854/) (2024)
- [Single microphone speaker extraction using unified time-frequency Siamese-Unet](https://mlsanity.com/paper/2203.02941/) (2022)

## Abstract (arXiv)

This paper introduces a multi-microphone method for extracting a desired speaker from a mixture involving multiple speakers and directional noise in a reverberant environment. In this work, we propose leveraging the instantaneous relative transfer function (RTF), estimated from a reference utterance recorded in the same position as the desired source. The effectiveness of the RTF-based spatial cue is compared with direction of arrival (DOA)-based spatial cue and the conventional spectral embedding. Experimental results in challenging acoustic scenarios demonstrate that using spatial cues yields better performance than the spectral-based cue and that the instantaneous RTF outperforms the DOA-based spatial cue.