HRTFInterpAE

Paper | Slides | Demo

This repository contains the implementation of "Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning" presented in IWAENC2022. Please cite [1] in your work when using this code in your experiments.

Requirements

We checked the code with the following computational environment.

Ubuntu 20.04.2 LTS
GeForce RTX 3090 (24GB VRAM)

Python 3.8.10

matplotlib==3.4.2
numpy==1.21.0
python-sofa==0.2.0
scipy==1.7.0
tensorboard==2.6.0
torch==1.9.0+cu111
torchaudio==0.9.0

Tutorial on Colab

You can test our pretrained model with a short tutorial notebook we have prepared.

Installation

Clone this repository.
Ensure that your computational environment meets the above or similar requirements. You can use conda and hrtfinterpae.yml to quickly build an environment.
```
conda env create -n hrtfinterpae -f hrtfinterpae.yml
conda activate hrtfinterpae
```
Download the HUTUBS HRTF database [2,3] and extract HRIRs.zip.

Locate the directories as shown below.

***/
  ├ HRTFInterpAE_public/
  └ HUTUBS/
    └HRIRs/

Test

To test our pretrainded model,

Move to HRTFInterpAE_public.
```
cd ***/***/HRTFInterpAE_public
```
Make a directory which contains output files.
```
mkdir -p outputs/model_0_test
```

Test the model.

nohup python3 -u main.py -c 0 -a outputs/model_0_test -test -lf 'outputs/model_0/hrtf_interp_ae.best.net' > 'outputs/model_0_test/log_test.txt' &

Train

To train our model with the HUTUBS database,

Move to HRTFInterpAE_public.
```
cd ***/***/HRTFInterpAE_public
```
Make a directory which contains output files.
```
mkdir -p outputs/model_0_train
```

Train a model.

nohup python3 -u main.py -c 0 -a outputs/model_0_train > 'outputs/model_0_train/log.txt' &

The log is written in outputs/model_0_train/log.txt.

You can see loss graphs in tensorboard by

tensorboard --logdir outputs/model_0_train/logs/***

The test will start automatically after the training.

To train a model with a new configuration,

Create a new dict config_* in configs.py.
* must be an integer.
With the option -c *, run main.py in the same way as above.

For other command-line arguments, see arg_parse() in main.py.

Note

Files in t_des are 3D Cartesian coordinates of the points contained in spherical t-design [4] and are obtained from [5].

Cite

@inproceedings{Ito:IWAENC2022,
  author    = {Yuki Ito and Tomohiko Nakamura and Shoichi Koyama and Hiroshi Saruwatari},
  title     = {Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning},
  booktitle = {in Proceedings of International Workshop on Acoustic Signal Enhancement},
  month     = {Sep.},
  year      = {2022},
  note      = {(accepted)}
}

License

CC-BY-4.0

References

[1] Yuki Ito, Tomohiko Nakamura, Shoichi Koyama, and Hiroshi Saruwatari, “Head-Related Transfer Function Interpolation from Spatially Sparse Measurements Using Autoencoder with Source Position Conditioning,” in Proc. International Workshop on Acoustic Signal Enhancement (IWAENC), Sep., 2022. (to appear) [PDF] [Slides]

[2] Fabian Brinkmann, Manoj Dinakaran, Robert Pelzer, Peter Grosche, Daniel Voss, and Stefan Weinzierl, “A cross-evaluated database of measured and simulated HRTFs including 3D head meshes, anthropometric features, and headphone impulse responses”, J. Audio Eng. Soc., vol. 67, no. 9, pp. 705–718, 2019.

[3] Fabian Brinkmann, Manoj Dinakaran, Robert Pelzer, Jan Joschka Wohlgemuth, Fabian Seipel, Daniel Voss, Peter Grosche, Stefan Weinzierl, “The HUTUBS head-related transfer function (HRTF) database,” 2019, url: http://dx.doi.org/10.14279/depositonce-8487 (accessed May 6, 2022).

[4] X. Chen and R. S. Womersley, “Existence of solutions to systems of underdetermined equations and spherical designs,” SIAM J. Numer. Anal., vol. 44, no. 6, pp. 2326–2341, 2006.

[5] https://www.polyu.edu.hk/ama/staff/xjchen/sphdesigns.html (accessed Sep. 18, 2022)

ikets / HRTFInterpAE_public