Blind Speech Separation and Dereverberation using Neural Beamforming

• URL: http://arxiv.org/abs/2103.13443v1
• Date: Wed, 24 Mar 2021 18:43:52 GMT
• Title: Blind Speech Separation and Dereverberation using Neural Beamforming
• Authors: Lukas Pfeifenberger and Franz Pernkopf
• Abstract summary: 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.
• Score: 28.7807578839021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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