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Youngdo Ahn's Projects

attention-cnn icon attention-cnn

Source code for "On the Relationship between Self-Attention and Convolutional Layers"

audiolm-pytorch icon audiolm-pytorch

Implementation of AudioLM, a SOTA Language Modeling Approach to Audio Generation out of Google Research, in Pytorch

cleanlab icon cleanlab

Finding label errors in datasets and learning with noisy labels.

crossdomainfewshot icon crossdomainfewshot

Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight)

db-aiat icon db-aiat

The implementation of "Dual-branch Attention-In-Attention Transformer for single-channel speech enhancement"

deepemd icon deepemd

Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020

domainbed icon domainbed

DomainBed is a suite to test domain generalization algorithms

emotion icon emotion

Tools for testing emotion recognition methods.

espnet icon espnet

End-to-End Speech Processing Toolkit

fluda icon fluda

Separate block diagrams for training and test phases.

im2wav icon im2wav

Implementation of the pipeline presented in I hear your true colors: Image Guided Audio Generation

jukemir icon jukemir

Perform transfer learning for MIR using Jukebox!

meta-weight-net icon meta-weight-net

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

nara_wpe icon nara_wpe

Different implementations of "Weighted Prediction Error" for speech dereverberation

selectivenet icon selectivenet

code for the ICML paper "SelectiveNet - A Deep Neural Network with an Integrated Reject Option"

skipvqvc icon skipvqvc

An implementation of SkipVQVC with various settings.

speech-transformer icon speech-transformer

A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.

tvlt icon tvlt

PyTorch code for β€œTVLT: Textless Vision-Language Transformer” (NeurIPS 2022)

usoms-e_life icon usoms-e_life

Baseline pipeline LiFE to reproduce the extracted linguistic features from the ComParE2020_USOMS-e challenge. We utilise and provide contextual word embeddings using a frozen (not fine-tuned) German Bidirectional Language Transformer (Bert).

vad icon vad

Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.

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