Pinned Repositories
opensmile
The Munich Open-Source Large-Scale Multimedia Feature Extractor
deeplearning.cs.cmu.edu
11-785 Website
denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
jhkonan.github.io
Fall 2019 Website for 11-785, Introduction to Deep Learning
opensmile-python
Python package for openSMILE
personal-website
Personal website for Joseph Konan
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Spring2019_Tutorials
VoIP-DNS-Challenge
FullSubNet-plus
The official PyTorch implementation of "FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement".
jhkonan's Repositories
jhkonan/jhkonan.github.io
Fall 2019 Website for 11-785, Introduction to Deep Learning
jhkonan/deeplearning.cs.cmu.edu
11-785 Website
jhkonan/denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
jhkonan/opensmile-python
Python package for openSMILE
jhkonan/personal-website
Personal website for Joseph Konan
jhkonan/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
jhkonan/Spring2019_Tutorials
jhkonan/VoIP-DNS-Challenge