Support material and source code for the system described in : "New Sonorities for Jazz Recordings: Separation and Mixing using Deep Neural Networks".
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Updated
Jul 19, 2017 - Python
Support material and source code for the system described in : "New Sonorities for Jazz Recordings: Separation and Mixing using Deep Neural Networks".
Support material and source code for the model described in : "A Recurrent Encoder-Decoder Approach With Skip-Filtering Connections For Monaural Singing Voice Separation"
Audio Signal Processing Python Tools
Singing Voice Separation via Recurrent Inference and Skip-Filtering Connections - PyTorch Implementation. Demo:
This is a project for Columbia Research Project
Control mechanisms to the U-Net architecture for doing multiple source separation instruments
Pytorch implementation of MDensenet and sparse NMF. Made for my undergraduate thesis "Music Source Separation with Supervised Learning Methods".
Tensorflow 2.x (with Keras API) Implementation of the TaSNet (Luo et al., 2018)
Pytorch implementation of subband decomposition
The code for the MaD TwinNet. Demo page:
KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021
Source Separation training codebase for the Sound Demixing Challenge 2023.
Intel OpenVINO competition
Sound Demixing Challenge 2023
Implementation of Demucs in PyTorch.
Create and explore isolated tracks from music files
Carnatic singing voice separation trained with in-domain data with leakage
Neural Networks for Interference Reduction in Multi-track Recordings. The Convolutional Autoencoders (CAEs) and the truncated-UNet (t-UNet).
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