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speech enhancement\speech seperation\sound source localization
implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" by pytorch
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.
HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks
Implementation for paper "iMetricGAN: Intelligibility Enhancement for Speech-in-Noise using Generative Adversarial Network-based Metric Learning"
PAGAN: a phase-adapted GAN for speech enhancement
Python implementation of the Short Term Objective Intelligibility measure
A python package for calculating the PESQ.
Self-Attention Generative Adversarial Network for Speech Enhancement using Tensorflow 2
SegAN: Semantic Segmentation with Adversarial Learning
Speech Enhancement Generative Adversarial Network in TensorFlow
SEGAN pytorch implementation https://arxiv.org/abs/1703.09452
Speech Enhancement Generative Adversarial Network in PyTorch
Speech Enhancement Metrics (PESQ, CSIG, CBAK, COVL)
Speech Algorithms
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