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Low-Latency Speaker-Independent Continuous Speech Separation
[article]
2019
arXiv
pre-print
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architecture combined with a double buffering scheme and (2) by performing enhancement with a set of fixed beamformers followed by a neural post-filter. ...
The previous SI-CSS method uses a neural network trained with permutation invariant training and a data-driven beamformer and thus requires much processing latency. ...
The proposed enhancement scheme, combining the fixed beamformers with the post-filter, was less sensitive to the degradation in the TF mask quality. ...
arXiv:1904.06478v1
fatcat:wjozo4mthvel7bpwuuoiw6y4ny
Low-latency Speaker-independent Continuous Speech Separation
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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This is achieved (1) by using a new speech separation network architecture combined with a double buffering scheme and (2) by performing enhancement with a set of fixed beamformers followed by a neural ...
The previous SI-CSS method uses a neural network trained with permutation invariant training and a data-driven beamformer and thus requires much processing latency. ...
The proposed enhancement scheme, combining the fixed beamformers with the post-filter, was less sensitive to the degradation in the TF mask quality. ...
doi:10.1109/icassp.2019.8682274
dblp:conf/icassp/YoshiokaCLXED19
fatcat:nvx2cfpbkrfr3jm2fv4f3v6ec4
Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks
[article]
2021
arXiv
pre-print
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The CNN architecture could leverage the spatial information and will be more region adaptive during the beamforming process. ...
A comprehensive performance comparison in terms of qualitative and quantitative measures for fully connected neural network (FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the ...
CONCLUSION This paper demonstrates the usefulness of an improvised CNN based beamforming method for ultrasound image quality enhancements in terms of contrast and resolution. ...
arXiv:2106.07006v2
fatcat:aclvijwpqzbxhbor7hna5ktiua
VMInNet: Interpolation of Virtual Microphones in Optimal Latent Space Explored by Autoencoder
2021
Journal of Signal Processing
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with high performance. ...
This technique has been shown to be effective in improving the speech enhancement performance of beamforming in underdetermined situations. ...
We use two-dimensional fully convolutional neural networks (CNNs) to design the autoencoder, which allows the network to handle inputs with arbitrary length and perform interpolation by taking the whole ...
doi:10.2299/jsp.25.245
fatcat:ao4ol5d2zbgbza6ebs22ccrram
SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing
[article]
2021
arXiv
pre-print
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The single channel speech enhancement is done in log spectral domain using convolution neural network (CNN)-long short term memory (LSTM) based architecture. ...
We propose a two stage method involving a beamformer followed by single channel enhancement. ...
This proposed model involving A-FaSNet based beamforming with the single channel enhancement pipeline also fares well when comparing with the enhanced baseline. ...
arXiv:2106.12763v1
fatcat:of4t4janf5d3besjpl2467x4nq
A Computer Vision Based Beamforming Scheme for Millimeter Wave Communication in LOS Scenarios
[article]
2020
arXiv
pre-print
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2019
2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT)
unpublished
doi:10.1109/iccsnt47585.2019.8962465 fatcat:q25vda5dpbbchbe5cpomsxkytu
doi:10.1109/iccsnt47585.2019.8962465 fatcat:q25vda5dpbbchbe5cpomsxkytu
A wireless coverage model is built to investigate the coverage performance and influence of positioning accuracy achieved by convolutional neural network (CNN) for image processing. ...
It is proved by simulations that the beamforming scheme is practicable and the mainstream CNN we employed is sufficient in both aspects of beam directivity accuracy and processing speed in frame per second ...
Location-aware communication aims to utilize the location information for enhancing the usage of spatial dimension including beamforming. 5G dense network has been proposed for learning location information ...
arXiv:2006.11566v1
fatcat:jjtvoot6yvfkpj34arrfwznuju
3-D Feature and Acoustic Modeling for Far-Field Speech Recognition
[article]
2020
arXiv
pre-print
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The 3-D CNN architecture allows the combination of multi-channel features that optimize the speech recognition cost compared to the traditional beamforming models that focus on the enhancement task. ...
The MAR features are fed to a convolutional neural network (CNN) architecture which performs the joint acoustic modeling on the three dimensions. ...
In order to enhance the learning of the non linearity in the filters of the 3-D CNN layer, we use the Network in Network (NIN) [38] architecture. ...
arXiv:1911.05504v2
fatcat:c66cew573vc7vng2hyhmfbuh4e
Hybrid Beamforming for MISO System via Convolutional Neural Network
2022
Electronics
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In this work, a low-complexity convolutional neural network (CNN)-based HBF algorithm is proposed to solve the SE maximization problem under the constant modulus constraint and transmit power constraint ...
Hybrid beamforming (HBF) is a promising approach to obtain a better balance between hardware complexity and system performance in massive MIMO communication systems. ...
Further, [18] solved three beamforming optimization problems using DL to enhance HBF performance. All the papers mentioned above employ deep supervised learning to train the network. ...
doi:10.3390/electronics11142213
fatcat:y2xxlvvfijeyvengc3w3hrcfqy
Multi-Channel Automatic Speech Recognition Using Deep Complex Unet
[article]
2020
arXiv
pre-print
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It also achieves superior performance than the recently proposed neural beamforming method. ...
The front-end module in multi-channel automatic speech recognition (ASR) systems mainly use microphone array techniques to produce enhanced signals in noisy conditions with reverberation and echos. ...
The acoustic model used in the back-end contains a 2-layer CNNs and a 12-layer time delay neural networks (TDNN). ...
arXiv:2011.09081v1
fatcat:ihsuw26r4nc2teoiyf45ovqeoi
End-to-End Multi-Channel Speech Enhancement Using Inter-Channel Time-Restricted Attention on Raw Waveform
2019
Interspeech 2019
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The model first extracts sample-level speech embedding using channel-wise convolutional neural network (CNN) and compensates time-delays between the channels based on the embedding, resulting in timealigned ...
This paper describes a novel waveform-level end-to-end model for multi-channel speech enhancement. ...
Although these methods introduced endto-end filter prediction networks, the networks were optimized with back-end acoustic model only to minimize ASR costs, not speech enhancement losses. ...
doi:10.21437/interspeech.2019-2397
dblp:conf/interspeech/LeeKKKK19
fatcat:jn4uba3kobhmtknojxjktg3yoi
Speech Enhancement Based on Beamforming and Post-Filtering by Combining Phase Information
2020
Interspeech 2020
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In this paper, a multi-channel speech enhancement method is proposed, which combines beamforming and post-filtering based on neural network. ...
With the development of microphone array signal processing technology and deep learning, the beamforming combined with neural network has provided a more diverse solution for this field. ...
In recent years, traditional beamforming methods have been combined with deep neural networks (DNNs) to improve the performance of speech enhancement. ...
doi:10.21437/interspeech.2020-0990
dblp:conf/interspeech/ChengB20
fatcat:3hjxi5b4indyheyv7ly6ayxzzy
Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks
[article]
2019
arXiv
pre-print
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We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). ...
Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices. ...
The second CNN (CNNRF) takes the input of the subsequent channel matrix with selected rows to choose RF beamformers. ...
arXiv:1905.03107v2
fatcat:j52rwhk2rzcevk2ofbrwrqwam4
Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency
2020
Ultrasonography
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Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. ...
Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations ...
The FUIQA system was implemented with two deep CNN models, L-CNN and C-CNN. ...
doi:10.14366/usg.20102
pmid:33152846
pmcid:PMC7758107
fatcat:7twnmbinjve7parztuzc3maes4
Intelligent Radio Signal Processing: A Survey
[article]
2021
arXiv
pre-print
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In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. ...
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive ...
A similar method to solve the SRM problem was used by proposing a CNN-based beamforming prediction network (BPNet) [148] . ...
arXiv:2008.08264v3
fatcat:4wmxyio6ejfvbfnqodq5z426m4
Neural networks for distant speech recognition
2014
2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA)
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We performed experiments on the AMI and ICSI meeting corpora, with results indicating that neural network models are capable of significant improvements in accuracy compared with discriminatively trained ...
In particular we investigate the use of convolutional and fully-connected neural networks with different activation functions (sigmoid, rectified linear, and maxout). ...
2 . 2 WER (%) on AMI and ICSI -MDM with beamforming System
AMI ICSI
BMMI GMM-HMM (LDA+STC) 54.8 46.8
DNN -Sigmoid
49.5 41.0
DNN -ReLU
46.3 38.7
DNN -Maxout
46.4 39.0
CNN -Sigmoid
46.3 39.5
CNN ...
doi:10.1109/hscma.2014.6843274
dblp:conf/hscma/RenalsS14
fatcat:zfjpq5de7fdzvbqc4p7vcng72m
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