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Blind Speech Separation and Dereverberation using Neural Beamforming

arXiv 2103.13443 ↗First posted 24 Mar 2021cs.SD cs.LG eess.AScited by 0

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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).

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.

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References · 26

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14 more
  1. 2018Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separationcited by 125
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  14. 2019W-Net BF: DNN-based Beamformer Using Joint Training Approachcited by 1

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