Improved MVDR Beamforming Using Single-Channel Mask Prediction Networks

By Hakan Erdogan, John R. Hershey, Shinji Watanabe, Michael I. Mandel, Jonathan Le Roux. Interspeech, 2016

350 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5e74d4e041a25e7752a596e2891975df5ba65aa2

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Citations in the corpus

SpeechBrain: A General-Purpose Speech Toolkit (2021, neutral): “The SCMs for speech and noise can be obtained using time-frequency masks [56, 103, 104].”
A Review of Speaker Diarization: Recent Advances with Deep Learning (2021, background): “9% in the LibriCSS meeting task based on mask-based MVDR beamforming [97, 98].”
A Survey of Sound Source Localization with Deep Learning Methods (2021, methodology): “Escolano et al. [2014] considered applying Bayesian inference on a Laplacian source mixture model, using GCC-PHAT features in a two-microphone array set-up.”
SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures (2019, methodology): “This procedure for neural network mask-based beamforming was proposed for speech denoising [44], [45] and has been shown to be very effective.”
Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques (2019, background): “Given a set of features extracted from the microphone signals at its input and the true class affiliations z , t f at the output, the network is trained to output the SPP , t c f [22], [23].”
Supervised Speech Separation Based on Deep Learning: An Overview (2017, neutral): “[42] trained an RNN for monaural speech en1370”
FaSNet: Low-Latency Adaptive Beamforming for Multi-Microphone Audio Processing (2019, background): “The third class comprises MB beamformers, speciﬁcally MVDR [13] and GEV [10] beamformers.”
Neural Target Speech Extraction: An overview (2023, methodology): “We briefly review the mask-based beamforming approach, which was introduced initially for noise reduction and BSS [42], [43].”
Multi-Channel Deep Clustering: Discriminative Spectral and Spatial Embeddings for Speaker-Independent Speech Separation (2018, methodology): “We also performed oracle minimum variance dis-tortionless response (MVDR) beamforming as in [12,34], but results were signiﬁcantly worse than the MCWF.”
Student-teacher network learning with enhanced features (2017, methodology): “Future work will consider extending the proposed method to handle sequence discriminative training and state sequence posteriors, as well as using state-of-the-art beamforming with mask estimation [20, 21, 22].”
Multi-Microphone Neural Speech Separation for Far-Field Multi-Talker Speech Recognition (2018, methodology): “In this paper, we examine two variants of mask-driven MVDR beamforming [6, 17].”
Speaker-Aware Neural Network Based Beamformer for Speaker Extraction in Speech Mixtures (2017, methodology): “Recently, there have been great advances in extracting speech from an observation in a noisy environment using a beamformer [6, 7, 8].”