Acoustic Beamformer using an Ad-Hoc array of smartphones
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Updated
Feb 2, 2023 - Python
Acoustic Beamformer using an Ad-Hoc array of smartphones
TF1 implementation of 'Phase-Aware Speech Enhancement with Deep Complex U-Net' paper
speech-enhancement-flask
Baselines for the UDASE task of the CHiME-7 challenge
A PyTorch implementation of "Improving noise robust automatic speech recognition with single-channel time-domain enhancement network"
code and detailed results for the paper Thoidis I., Vrysis L., Markou D., Papanikolaou, G. Temporal Auditory Coding Features for Causal Speech Enhancement. Electronics 2020, 9, 1698.
语音前端仓库 || a modified version of Asteroid toolkit for Speech Front-end
Improved speech enhancement with the Wave-U-Net, a deep convolutional neural network architecture for audio source separation, implemented for the task of speech enhancement in the time-domain.
unofficial implementation of "A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement"
[Research] 2nd place solution at L3DAS21 challenge Task 1. Using FCN architecture and Perceptual Losses. Implemented with the SpeechBrain toolkit
masters thesis on deep learning methods for speech enhancement
Pytorch Models for Speech Enhancement
Open implementation of UNIVERSE and UNIVERSE++ diffusion-based speech enhancement models.
unofficial implementation of "CPTNN: CROSS-PARALLEL TRANSFORMER NEURAL NETWORK FOR TIME-DOMAIN SPEECH ENHANCEMENT"
This repository contains a PyTorch implementation of U-Net applied on mel-spectograms of audio files for speech denoising.
We implemented the DEMUCS model for speech enhancement in the time-frequency domain, and additionally implemented HD-DEMUCS.
Audio processing project
A Fast Speech Enhancement toolkit using Conv-TasNet
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