Adaptive and Focusing Neural Layers for Multi-Speaker Separation Problem
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Updated
Jul 7, 2018 - Jupyter Notebook
Adaptive and Focusing Neural Layers for Multi-Speaker Separation Problem
A PyTorch implementation of Time-domain Audio Separation Network (TasNet) with Permutation Invariant Training (PIT) for speech separation.
Deezer source separation library including pretrained models.
A convolutional neural network for blind audio source separation.
Download multiple tracks from youtube by a single query - with GUI.
Software that performs the separation of vocals from music using neural networks (part of my Bachelor's thesis).
Deep Recurrent Neural Networks for Source Separation
Two-talker Speech Separation with LSTM/BLSTM by Permutation Invariant Training method.
A PyTorch implementation of DNN-based source separation.
Code and datasets for 'Move2Hear: Active Audio-Visual Source Separation' (ICCV 2021)
An implementation of audio source separation tools.
An exploration of blind source audio separation using spiking neural networks. Latency, power. and intelligibility are primary objectives while bio-plausibility is left as a secondary objective to be addressed in the future.
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Youtube Audio Downloader and Separator
OpenVINO DevCUP music aeparation & transcription
Unofficial PyTorch implementation of Google AI's VoiceFilter system
Clojure bindings for PyTorch implementation of Open-Unmix
The PyTorch-based audio source separation toolkit for researchers
Ultimate Vocal Remover for Google Colab
logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.
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