Pinned Repositories
ClarityChallenge2023
Speech Enhancement for Hearing Aid
CS224n-Natural-Language-Processing
Stanford CS224n: Natural Language Processing, 2018 & 2021 solution
Digital-Filter-Design
Filter design for music equalizer and Examples of DSP
digital-signal-processing-lecture
Digital Signal Processing - Theory and Computational Examples
doasvm-visualizer
Visualization demo of the doasvm direction-of-arrival estimator
Docs-for-SpeechEnhancement-and-TinyML
Documents for Speech Enhancement with Machine leanring and TinyML
friture
Real-time audio visualizations (spectrum, spectrogram, etc.)
Speech-Enhancement-Pytorch
Pytorch Models for Speech Enhancement
Speech-Enhancement-TF
ML Model for Speech Enhancement: Tensorflow 2.x implementation of the paper
Speech-evaluation-methods
Understanding the metrics for evaluating speech
ooshyun's Repositories
ooshyun/Speech-Enhancement-Pytorch
Pytorch Models for Speech Enhancement
ooshyun/ClarityChallenge2023
Speech Enhancement for Hearing Aid
ooshyun/Speech-evaluation-methods
Understanding the metrics for evaluating speech
ooshyun/Docs-for-SpeechEnhancement-and-TinyML
Documents for Speech Enhancement with Machine leanring and TinyML
ooshyun/Digital-Filter-Design
Filter design for music equalizer and Examples of DSP
ooshyun/Speech-Enhancement-TF
ML Model for Speech Enhancement: Tensorflow 2.x implementation of the paper
ooshyun/6s965-fall2022
ooshyun/app-openearable
ooshyun/bazel-templete
ooshyun/clearbuds-fw
ooshyun/code-ex
Practice for coding test with C++ and Python
ooshyun/crossenv
Cross-compiling virtualenv for Python
ooshyun/cuda-example
ooshyun/dashboard-openearable
ooshyun/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.
ooshyun/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."
ooshyun/git-flow-example
ooshyun/ivy
Unified AI
ooshyun/libtensorflow_cc
Pre-built libtensorflow_cc.so and Docker Images for TensorFlow C++ API
ooshyun/LLaMA2-7B-on-laptop
Lab 5 project of MIT-6.5940, deploying LLaMA2-7B-chat on one's laptop with TinyChatEngine.
ooshyun/Make-and-CMake-Examples
Example of makefile for make and CMake in vscode
ooshyun/makefile_sample
ooshyun/mit-tinyml-6s965-fall2022-lab4
ooshyun/ooshyun.github.io
💎 🐳 A super customizable Jekyll theme for personal site, team site, blog, project, documentation, etc.
ooshyun/ooshyun.github.io.comments
Repository for ooshyun.github.io comments
ooshyun/open-earable
ooshyun/PicoMusic
Music generation on RP2040 with CMSIS-Stream, CMSIS-DSP and Arm-2D
ooshyun/profiling-example
ooshyun/submodule-test
ooshyun/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