Seunghyun Oh 's repositories
Speech-Enhancement-Pytorch
Pytorch Models for Speech Enhancement
ClarityChallenge2023
Speech Enhancement for Hearing Aid
Speech-evaluation-methods
Understanding the metrics for evaluating speech
Docs-for-SpeechEnhancement-and-TinyML
Documents for Speech Enhancement with Machine leanring and TinyML
Digital-Filter-Design
Filter design for music equalizer and Examples of DSP
Speech-Enhancement-TF
ML Model for Speech Enhancement: Tensorflow 2.x implementation of the paper
code-ex
Practice for coding test with C++ and Python
crossenv
Cross-compiling virtualenv for Python
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.
FullSubNet-Tensorflow
Convert PyTorch to Tensorflow for TFLite from "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
ivy
Unified AI
LLaMA2-7B-on-laptop
Lab 5 project of MIT-6.5940, deploying LLaMA2-7B-chat on one's laptop with TinyChatEngine.
Make-and-CMake-Examples
Example of makefile for make and CMake in vscode
ooshyun.github.io
💎 🐳 A super customizable Jekyll theme for personal site, team site, blog, project, documentation, etc.
ooshyun.github.io.comments
Repository for ooshyun.github.io comments
PicoMusic
Music generation on RP2040 with CMSIS-Stream, CMSIS-DSP and Arm-2D
tinyengine
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory