Deep Acoustic Repo
This repo contains python code for our TVCG paper "Scene-Aware Audio Rendering via Deep Acoustic Analysis". We include the training and testing code for reverberation (T60) and equalization (EQ) analysis. The conference talk at IEEE VR 2020 is here.
Preparation
First step is to download data. cd into the data folder under this repo and run ./download_data.sh. The dataset takes 5.5GB disk space. The code uses extracted features instead of raw audio files. To reproduce the dataset, please refer to our paper and retrieve impulse responses from the ACE Challenge, MIT IR Survey, and human speech from the DAPS dataset, as described in our paper. We do not re-distribute these data here.
Then you need to create the python environment. We recommend using conda:
$ conda env create -f environment.yml -n deepacoustic
$ conda activate deepacoustic
Usage
We provide two trained models in the models folder. You can use them for inference already, or train from scratch by not providing the -m (model) argument to the training script. You can provide -h argument to see help messages.
usage: train_subbands_T60_model_pre-feature [-h] [--input INPUT]
[--output OUTPUT]
[--target TARGET] [--label LABEL]
[--model MODEL]
Script to train subband T60 prediction model using precomptued features
optional arguments:
-h, --help show this help message and exit
--input INPUT, -i INPUT
Path to the precomputed feature and label file
--output OUTPUT, -o OUTPUT
Directory to write results
--target TARGET, -t TARGET
Target band to predict (1~8), 0=fullband (default),
9=selected bands
--label LABEL, -l LABEL
Which label to use for training
--model MODEL, -m MODEL
Path to an existing model, or None
example:
python train_subbands_T60_model_pre-feature.py -i data/pre_features.h5 -t 0 -l t60
and the test/inference function:
usage: test_subbands_T60_model_pre-feature [-h] [--input INPUT] --model MODEL
[--target TARGET] --label
{modulation,t60}
Script to test subband T60 prediction model
optional arguments:
-h, --help show this help message and exit
--input INPUT, -i INPUT
Path to the precomputed feature and label file
--model MODEL, -m MODEL
Path to the trained model
--target TARGET, -t TARGET
Target band to predict (1~8), 0=fullband (default),
9=selected bands
--label {modulation,t60}, -l {modulation,t60}
Which label to use for training
example:
python test_subbands_T60_model_pre-feature.py -i data/pre_features.h5 -m models/t60.hdf5 -l t60 -t 0
After running this, a result csv file will be written in the same folder as the model.
Citation
If you use our code or models, please consider citing:
@article{tang2020scene,
title={Scene-Aware Audio Rendering via Deep Acoustic Analysis},
author={Tang, Zhenyu and Bryan, Nicholas J and Li, Dingzeyu and Langlois, Timothy R and Manocha, Dinesh},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2020},
publisher={IEEE}
}
Contributing
Contributions are welcomed! Read the Contributing Guide for more information.
Licensing
This project is licensed under the Apache V2 License. See LICENSE for more information.