• Stars
    star
    7
  • Rank 2,283,065 (Top 46 %)
  • Language
    Python
  • License
    GNU General Publi...
  • Created over 6 years ago
  • Updated over 6 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Latex Code of thesis tools for manipulation

More Repositories

1

sudo_rm_rf

Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of separating sources from mixtures.
Jupyter Notebook
303
star
2

two_step_mask_learning

A two step optimization for sound source separation on the adaptive front-end domain
Python
66
star
3

unsup_speech_enh_adaptation

Unsupervised domain adaptation for conversational speech enhancement using RemixIT
Jupyter Notebook
51
star
4

fedenhance

Code for the paper: Separate but togerher: Unsupervised Federated Learning for Speech Enhancement from non-iid data
Jupyter Notebook
39
star
5

heterogeneous_separation

Code and data recipes for the paper: Heterogeneous Target Speech Separation
Python
38
star
6

unsupervised_spatial_dc

Code for the paper: "Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information"
Python
21
star
7

biased_separation

Code for the paper: Unified Gradient Reweighting for Model Biasing with Applications to Source Separation
Python
14
star
8

optimal_condition_training

Code and data recipes for the paper: Optimal Condition Training for Target Source Separation by Efthymios Tzinis, Gordon Wichern, Paris Smaragdis and Jonathan Le Roux
Python
12
star
9

nldrp

Non linear dynamics for emotion classification
Python
8
star
10

activelearning

Active Learning for Emotionally Salient Utterances and Segments using Text & Audio
Python
4
star
11

bootstrapped_mds

Bootstrapped MDS: A Coordinate Search Algorithm for Multidimensional Scaling which optimizes Stress by evaluating the function multiple times over different coordinates but also bootstraping over previous successful iterations. With this algorithm there is a probability of evaluating the function alongside a coordinate step depending on the previous successful evaluations across this coordinate.
Jupyter Notebook
2
star
12

pat_rec_ntua

Labs exercises in NTUA (2016-2017) for the Pattern Recognition course 9th semester Contributors: Efthymios Tzinis Konstantinos Kallas
MATLAB
1
star