This repository contains code to train a mono-to-binaural neural sound renderer. If you use this code or the provided dataset, please cite our paper "Neural Synthesis of Binaural Speech from Mono Audio",

@inproceedings{richard2021binaural,
  title={Neural Synthesis of Binaural Speech from Mono Audio},
  author={Richard, Alexander and Markovic, Dejan and Gebru, Israel D and Krenn, Steven and Butler, Gladstone and de la Torre, Fernando and Sheikh, Yaser},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

For a qualitative comparison to our work, check out our supplemental video here.

Download the dataset and unzip it. When unzipped, you will find a directory containing the training data for all eight subjects and a directory containing the test data for these eight subjects plus an additional validation sequence.

Each subject's directory contains the transmitter mono signal as mono.wav, the binaural recordings for the receiver, binaural.wav, and two position files for transmitter and receiver. The audio files are 48kHz recordings and the position files have tracked receiver and transmitter head positions and orientations at a rate of 120Hz, such that there is a new receiver/transmitter position every 400 audio samples.

The position files have one tracked sample per row. So, 120 rows represent 1 second of tracked positions. Positions are represented as (x,y,z) coordinates and head orientations are represented as quaternions (qx, qy, qz, qw). Each row therefore contains seven float values (x,y,z,qx,qy,qz,qw).

Note that in our setup the receiver was a mannequin that did not move. Receiver positions are therefore the same at all times. The receiver is the in the origin of the coordinate system and, from the receiver's perspective, x points forward, y points right, and z points up.

• tqdm
• numpy
• scipy
• torch (v1.7.0)
• torchaudio (v0.7.0)

The training can be started by running the train.py script. Make sure to pass to correct command line arguments:

• --dataset_directory: the path to the directory containing the training data, i.e. /your/downloaded/dataset/path/trainset
• --artifacts_directory: the path to write log files to and to save models and checkpoints
• --num_gpus: the number of GPUs to be used; we used four for the experiments in the paper. If you train on less GPUs or on GPUs with low memory, you might need to reduce the batch size in train.py.
• --blocks: the number of wavenet blocks of the network. Use 3 for the network from the paper or 1 for a lightweight, faster model with slightly worse results.

The evaluation can be started by running the evaluate.py script. Make sure to pass the correct command line arguments:

• --dataset_directory: the path to the directory containing the test data, i.e. /your/downloaded/dataset/path/testset
• --model_file: the path to the model you want to evaluate, will usually be located in the artifacts_dir used in the training script.
• --artifacts_directory: the generated binaural audio of each test sequence will be saved to this directory.
• --blocks: the number of wavenet blocks of the network.

We provide silent videos for each of the test sequences here for you to visualize your results. To generate a top-view video for your generated audio similar to the videos shown in our supplemental material, you might use ffmpeg:

 ffmpeg -i <silent_video.mp4> -i <binaural_audio.wav> -c:v copy -c:a aac output.mp4

We provide two pretrained models here.

The small model with just one wavenet block will give these results with the evaluation script:

l2 (x10^3):     0.197
amplitude:      0.043
phase:          0.862

The large model with three wavenet blocks will give these results with the evaluation script:

l2 (x10^3):     0.144
amplitude:      0.036
phase:          0.804

The code and dataset are released under CC-NC 4.0 International license.