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yhzdsk's starred repositories

Transfer-Learning-Library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Language:PythonMIT329400

AudioClassification-Pytorch

The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.

Language:PythonApache-2.036400

CleanUNet

Official PyTorch Implementation of CleanUNet (ICASSP 2022)

Language:PythonMIT28100

resemble-enhance

AI powered speech denoising and enhancement

Language:PythonMIT118300

Wave-U-Net

Implementation of the Wave-U-Net for audio source separation

Language:PythonMIT81500

Conv-TasNet

A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).

Language:PythonMIT65800

Spider-Monkey-Whinny-Detection

Code to replicate the experiments of the Interspeech 2021 paper "Multi-Attentive Detection of the Spider Monkey Whinny in the (Actual) Wild".

Language:PythonGPL-3.0200

sed-crnn

Single and multichannel sound event detection using convolutional recurrent neural networks. DCASE 2017 real-life sound event detection winning method.

Language:PythonNOASSERTION18300

speechbrain

A PyTorch-based Speech Toolkit

Language:PythonApache-2.0840600

voxceleb_trainer

In defence of metric learning for speaker recognition

Language:PythonMIT101700

ECAPA-TDNN

Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Language:PythonMIT57200

DENet

This repository contains the code related to the paper 'DENet: a deep architecture for audio surveillance applications'.

Language:PythonNOASSERTION4100

sed_eval

Evaluation toolbox for Sound Event Detection

Language:PythonMIT13700

auditok

An audio/acoustic activity detection and audio segmentation tool

Language:PythonMIT72600

DeepFilterNet

Noise supression using deep filtering

Language:PythonNOASSERTION227300

crnn-audio-classification

UrbanSound classification using Convolutional Recurrent Networks in PyTorch

Language:PythonMIT37800

Noise2Noise-audio_denoising_without_clean_training_data

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.

Language:Jupyter NotebookMIT17000

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