The PyTorch-based audio source separation toolkit for researchers
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Updated
Apr 10, 2024 - Python
The PyTorch-based audio source separation toolkit for researchers
Unofficial PyTorch implementation of Google AI's VoiceFilter system
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Deep Convolutional Neural Networks for Musical Source Separation
Collection of EM algorithms for blind source separation of audio signals
Speech Enhancement based on DNN (Spectral-Mapping, TF-Masking), DNN-NMF, NMF
A PyTorch implementation of DNN-based source separation.
This repository contains audio samples and supplementary materials accompanying publications by the "Speaker, Voice and Language" team at Google.
A neural network for end-to-end music source separation
A PyTorch implementation of Time-domain Audio Separation Network (TasNet) with Permutation Invariant Training (PIT) for speech separation.
target speaker extraction and verification for multi-talker speech
SEGAN pytorch implementation https://arxiv.org/abs/1703.09452
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
Singing-Voice Separation From Monaural Recordings Using Deep Recurrent Neural Networks
hyperspectral galaxy modeling and deblending
Unofficial PyTorch implementation of Music Source Separation with Band-split RNN
PyTorch code to separate instruments from music using a low-latency neural network
Tools of soundscape information retrieval, this repository is a developing project. Please go to https://github.com/meil-brcas-org/soundscape_IR for full releases.
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