# Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism

arXiv: https://arxiv.org/abs/2102.03762 · HTML: https://mlsanity.com/paper/2102.03762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [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)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [Wavesplit: End-to-End Speech Separation by Speaker Clustering](https://mlsanity.com/paper/2002.08933/) (2020)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [WHAMR!: Noisy and Reverberant Single-Channel Speech Separation](https://mlsanity.com/paper/1910.10279/) (2019)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [Voice Separation with an Unknown Number of Multiple Speakers](https://mlsanity.com/paper/2003.01531/) (2020)
- [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)
- [Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning](https://mlsanity.com/paper/2003.03927/) (2020)
- [Interrupted and cascaded permutation invariant training for speech separation](https://mlsanity.com/paper/1910.12706/) (2019)
- [On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments](https://mlsanity.com/paper/2011.05958/) (2020)
- [Speaker-Conditional Chain Model for Speech Separation and Extraction](https://mlsanity.com/paper/2006.14149/) (2020)

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

- [Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation](https://mlsanity.com/paper/2106.07843/) (2021)
- [Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation](https://mlsanity.com/paper/2307.12231/) (2023)

## Abstract (arXiv)

In this paper, we present a novel multi-channel speech extraction system to simultaneously extract multiple clean individual sources from a mixture in noisy and reverberant environments. The proposed method is built on an improved multi-channel time-domain speech separation network which employs speaker embeddings to identify and extract multiple targets without label permutation ambiguity. To efficiently inform the speaker information to the extraction model, we propose a new speaker conditioning mechanism by designing an additional speaker branch for receiving external speaker embeddings. Experiments on 2-channel WHAMR! data show that the proposed system improves by 9% relative the source separation performance over a strong multi-channel baseline, and it increases the speech recognition accuracy by more than 16% relative over the same baseline.