okrio Goto Github PK
Name: Okrio
Type: User
Bio: speech enhancement, acoustic echo cancellation, noise suppression, source localization, microphone array process
Location: Hangzhou China
Okrio's Projects
beamformers
Easy to use Beamformers for multi-channel speech separation/enhancement
beamforming-graduation-project
beamformit
BeamformIt acoustic beamforming software
binaural_localization
ITD-based localization of sound sources in complex acoustic environments
bird
Big Impulse Response Dataset
blep-ga-perceptual
Band limited step functions using IRs from optimized filters arrived through genetic algorithm search
books
技术书籍
bsie_toolbox
Blind System Identification and Equalization Toolbox
bss2014
频域盲源分离算法研究及其在高速列车噪声成分分离中的应用
bss_ctf_em
bss_ctf_lasso
bssaec2020
A New Perspective of Auxiliary-Function-Based Independent Component Analysis in Acoustic Echo Cancellation
cdr-dereverb
Coherence-based Dereverberation for Speech Enhancement
cgmm_mvdr
cheatsheets
Official Matplotlib cheat sheets
ci_sdr
ciglet
Lightweight signal processing library for audio and speech applications
cjson
Ultralightweight JSON parser in ANSI C
cmsis_5
CMSIS Version 5 Development Repository
cnn-for-single-channel-speech-enhancement
Convolutional neural nets for single channel speech enhancement
comparison-of-blind-source-separation-techniques
Compare AIRES BSS with TRINICON, ILRMA and AuxIVA (online and offline versions)
conv-tasnet
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
coursera-ml-andrewng-notes
吴恩达老师的机器学习课程个人笔记
crepe
CREPE: A Convolutional REpresentation for Pitch Estimation -- pre-trained model (ICASSP 2018)
criterion
A cross-platform C and C++ unit testing framework for the 21st century
cruse
a lightweight network for monaural speech enhancement
ctf_mint
cts-net
The supplemental material and samples with respect to the paper "TWO HEADS ARE BETTER THAN ONE: A TWO-STAGE APPROACH FOR MONAURAL NOISE REDUCTION IN THE COMPLEX DOMAIN" are provided (submitted to ICASSP 2021)
d2l-zh
《动手学深度学习》，英文版即伯克利深度学习（STAT 157，2019春）教材。面向中文读者、能运行、可讨论。
danet-tensorflow
Tensorflow implementation of "Speaker-independent Speech Separation with Deep Attractor Network"
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