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Multichannel Subband-Fullband Gated Convolutional Recurrent Neural Network For Direction-Based Speech Enhancement With Head-Mounted Microphone Arrays

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# Multichannel Subband-Fullband Gated Convolutional Recurrent Neural Network For Direction-Based Speech Enhancement With Head-Mounted Microphone Arrays

This repository contains the code for reproducing the results shown in the paper "Multichannel Subband-Fullband Gated Convolutional Recurrent Neural Network For Direction-Based Speech Enhancement With Head-Mounted Microphone Arrays".

[WASPAA Paper](https://doi.org/10.1109/WASPAA58266.2023.10248181)

[Demo](https://mc-directional-hma-speech-enhancement.iem.sh)

## Note
The code in the `spear-tools` submodule is subject to its own licenses. If not licensed, all rights remain with the original author (Imperial College London).

The `FullSubNet` sub-repository also is third-party code and has separate licensing.

## Installation
First `cd` into `./spear-tools` and set up the SPEAR paths and symbolic links and the conda environment according to `spear-tools/README.md`.

### Instructions
To run the models, you will need the packages defined in `requirements.txt`. Install them into your environment `<your-env-name>` using `pip install -r requirements.txt`.

For inference with the `MaxDirAndFullsubnet` method, you need to download the FullSubNet checkpoint model weights [here](https://github.com/Audio-WestlakeU/FullSubNet/releases/download/v0.2/fullsubnet_best_model_58epochs.tar) into `./FullSubNet`.

Model checkpoints are available [here](https://drive.google.com/file/d/107Yryam40MQQ2yndP_LXVAC_NFqfPC6X/view?usp=sharing)

Please adjust paths and other variables in the scripts `train.py`, `validate.py`, `process`, `validate_baseline_unprocessed.py`, `view_metrics.py` and in the config files as you need.

## Author
This repository is authored by Benjamin Stahl and was created at the Intitute of Elctronic Music and Acoustics in Graz, Austria in 2022/23.

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