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daipi2020's Projects

audio icon audio

Data manipulation and transformation for audio signal processing, powered by PyTorch

cnn-audio-denoiser icon cnn-audio-denoiser

Tensorflow 2.0 implementation of the paper: A Fully Convolutional Neural Network for Speech Enhancement

cubicsdr icon cubicsdr

Cross-Platform Software-Defined Radio Application

cusignal icon cusignal

cuSignal - RAPIDS Signal Processing Library

cyberradio icon cyberradio

📻 An SDR Based FM/AM Radio For Desktop. Accelerated with #cuSignal and Numba.

dcunet icon dcunet

Phase-aware speech enchancement with Deep Complex U-Net

denoiser icon 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.

dnp icon dnp

Audio Denoising with Deep Network Priors

pdgan icon pdgan

This project first presents a coherent signal demodulation method based on generative adversarial networks (GAN), called a phase demodulation generative adversarial network (PDGAN). We applyed GAN method to the field of Doppler signal demodulation for laser voice detection.Demodulation of the Doppler signal from a coherent signal is accomplished through unsupervised learning within the PDGAN, layer by layer, with global supervised feedback learning for fine-tuning. Drawing upon the adversarial principle of GAN, we let the coherent signal, Z, serve as an input for G and let the generated demodulated Doppler signal G(Z) and the clean Doppler signal X serve as the input for D. By means of alternate training and optimization, G learns the mapping relationship between the coherent signal, Z, and the Doppler signal, X, thereby achieving the goal of demodulating the coherent signal. This project mainly includes related data sets of Doppler signal and corresponding coherent signal. The structure and detailed description of the network are planned to be published in the Journal of optical engineering. The author can also be contacted for information [email protected]

pyemd icon pyemd

Python implementation of Empirical Mode Decompoisition (EMD) method

pyradio icon pyradio

🤓 Python Scripts for WBFM and AM demodulation. Accelerated with #cuSignal and Numba.

pysepm icon pysepm

Python implementation of performance metrics in Loizou's Speech Enhancement book

pytorch-kaldi icon pytorch-kaldi

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

rnn icon rnn

pytorch user defined RNN

rnn-speech-denoising icon rnn-speech-denoising

Recurrent neural network training for noise reduction in robust automatic speech recognition

segan icon segan

A PyTorch implementation of SEGAN based on INTERSPEECH 2017 paper "SEGAN: Speech Enhancement Generative Adversarial Network"

simpleaudiodenoise icon simpleaudiodenoise

A Simple and Efficient Implementation Of Fast Fourier Transform For Audio Denoise

soapysdr icon soapysdr

Vendor and platform neutral SDR support library.

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