Bongjun Kim's starred repositories
Working-with-the-Web-Audio-API
Various simple Web Audio API examples
ismir-2021-tutorial-case-studies
Code for the ISMIR 2021 tutorial "Programming MIR Baselines from Scratch: Three Cases Studies"
torchsynth
A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
evidential-deep-learning
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
deepbayes-2019
Practical assignments of the Deep|Bayes summer school 2019
DALLE-pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
taming-transformers
Taming Transformers for High-Resolution Image Synthesis
dominate
Dominate is a Python library for creating and manipulating HTML documents using an elegant DOM API. It allows you to write HTML pages in pure Python very concisely, which eliminate the need to learn another template language, and to take advantage of the more powerful features of Python.
tensor-sensor
The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
ml-surveys
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
DNS-Challenge
This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.
uncertainty-toolbox
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
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.
awesome-creative-coding
Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.
annotated-transformer
An annotated implementation of the Transformer paper.
ismir2020-metric-learning
ISMIR 2020 Tutorial for Metric Learning in MIR