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Esteban Gómez's Projects

18-327-wavelets-filter-banks icon 18-327-wavelets-filter-banks

This repository is an implementation, using Python with numpy, scipy, matlibplot, PyWavelets and GNU Octave, of MIT OCW 18.327 spring 2003 Wavelets, Filter Banks, and Applications.

2021 icon 2021

ASVspoof 2021 Baseline Systems

adm-osc icon adm-osc

An OSC dictionnary that implements the Audio Definition Model (ADM)

attentions icon attentions

PyTorch implementation of some attentions for Deep Learning Researchers.

audiowaveform icon audiowaveform

C++ program to generate waveform data and render waveform images from audio files

awesome-speech-enhancement icon awesome-speech-enhancement

A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.

bbwexnet icon bbwexnet

Low-complexity Real-time Neural Network for Blind Bandwidth Extension of Wideband Speech demo page

blackhole icon blackhole

BlackHole is a modern macOS virtual audio driver that allows applications to pass audio to other applications with zero additional latency.

cheat-sheets icon cheat-sheets

My personal Cheat-Sheets for various projects, tools and technologies

clpcnet icon clpcnet

Pitch-shifting, time-stretching, and vocoding of speech with Controllable LPCNet (CLPCNet)

conv-stft icon conv-stft

A STFT/iSTFT written up in PyTorch using 1D Convolutions

conv_arithmetic icon conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

cppflow icon cppflow

Run TensorFlow models in C++ without installation and without Bazel

ddsp_pytorch icon ddsp_pytorch

Implementation of Differentiable Digital Signal Processing (DDSP) in Pytorch

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.

dns-challenge icon dns-challenge

This repo contains the scripts, models, and required files for the ICASSP 2021 Deep Noise Suppression (DNS) Challenge.

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