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Stars1
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LanguageJava
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License
MIT License -
Created over 6 years ago
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Updated over 4 years ago
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LightNet
LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)LightNetPlusPlus
LightNet++: Boosted Light-weighted Networks for Real-time Semantic SegmentationNNAEC-NeuralNetworkbasedAcousticEchoCancellation
NNAEC-Neural Network based Acoustic Echo CancellationEfficientNet.PyTorch
Concise, Modular, Human-friendly PyTorch implementation of EfficientNet with Pre-trained Weights.ConvolutionaNeuralNetworksToEnhanceCodedSpeech
In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.MixNet-PyTorch
Concise, Modular, Human-friendly PyTorch implementation of MixNet with Pre-trained Weights.SharpPeleeNet
ImageNet pre-trained SharpPeleeNet can be used in real-time Semantic Segmentation/Objects DetectionLove Open Source and this site? Check out how you can help us
