# Blind Speech Separation and Dereverberation using Neural Beamforming

arXiv: https://arxiv.org/abs/2103.13443 · HTML: https://mlsanity.com/paper/2103.13443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 73 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [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)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Deep Speaker: an End-to-End Neural Speaker Embedding System](https://mlsanity.com/paper/1705.02304/) (2017)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [Learning Spread-out Local Feature Descriptors](https://mlsanity.com/paper/1708.06320/) (2017)
- [Deep attractor network for single-microphone speaker separation](https://mlsanity.com/paper/1611.08930/) (2016)
- [Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks](https://mlsanity.com/paper/1810.03655/) (2018)
- [How to Train Triplet Networks with 100K Identities?](https://mlsanity.com/paper/1709.02940/) (2017)
- [Triplet Network with Attention for Speaker Diarization](https://mlsanity.com/paper/1808.01535/) (2018)
- [A unified convolutional beamformer for simultaneous denoising and dereverberation](https://mlsanity.com/paper/1812.08400/) (2018)
- [Low-Latency Speaker-Independent Continuous Speech Separation](https://mlsanity.com/paper/1904.06478/) (2019)
- [W-Net BF: DNN-based Beamformer Using Joint Training Approach](https://mlsanity.com/paper/1910.14262/) (2019)

## Abstract (arXiv)

In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network. Speaker separation is guided by a set of predefined spatial cues. Dereverberation is performed by using neural beamforming, and speaker identification is aided by embedding vectors and triplet mining. We introduce a frequency-domain model which uses complex-valued neural networks, and a time-domain variant which performs beamforming in latent space. Further, we propose a block-online mode to process longer audio recordings, as they occur in meeting scenarios. We evaluate our system in terms of Scale Independent Signal to Distortion Ratio (SI-SDR), Word Error Rate (WER) and Equal Error Rate (EER).