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dyang's Projects

advisor icon advisor

Open-source implementation of Google Vizier for hyper parameters tuning

air-asvspoof icon air-asvspoof

Implementation of the paper "One-class Learning Towards Synthetic Voice Spoofing Detection"

alice icon alice

Automatic LInguistic Unit Count Estimator (ALICE)

asv-anti-spoofing-dada icon asv-anti-spoofing-dada

Dual-Adversarial Domain Adaptation for replay spoofing detection in automatic speaker verification.

asvspoof2019 icon asvspoof2019

Our submission to the ASVspoof 2019: Automatic Speaker Verification Spoofing and Countermeasures Challenge

audioage icon audioage

Transferring audio features to build models for rare conditions with scarce data

audiofile icon audiofile

A simple header-only C++ library for reading and writing audio files.

audiogpt icon audiogpt

AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

augly icon augly

A data augmentations library for audio, image, text, and video.

auto-age-labeler icon auto-age-labeler

A web application that uses artificial intelligence to automatically label voice datasets with the age of the speaker.

autogluon icon autogluon

AutoGluon: AutoML Toolkit for Deep Learning

auxiva-iss-dnn icon auxiva-iss-dnn

Code to reproduce the results in the paper "Surrogate Source Model Learning for Determined Source Separation"

bwe_fftnet icon bwe_fftnet

Implementation of Learning Bandwidth Expansion Using Perceptually-Motivated Loss (ICASSP 2019)

cgmm-mvdr icon cgmm-mvdr

Implementation of the CGMM-MVDR beamforming

create_wsj1_2345_db icon create_wsj1_2345_db

Collection of scripts to create a dataset of noisy multi-channel reverberant mixtures based on wsj1 and CHiME3 datasets.

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.

drum_sound_classifier icon drum_sound_classifier

A python module for making pandas datasets out of drum libraries, and training drum type classification models using a few different methods.

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