FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR (ICASSP 2022)

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given rectangular acoustic environment. Our model is inspired by StackGAN architecture. The audio examples and spectrograms of the generated RIRs are available here.

NEWS:

1) We have generalized our FAST-RIR to generate RIRs for any 3D indoor scenes represented using meshes. Official code of our network MESH2IR is available.

2) We release Speech2IR estimator. The official code of our network Speech2IR is available.

Requirements

Python3.6
Pytorch
python-dateutil
easydict
pandas
torchfile
gdown
librosa
soundfile
acoustics
wavefile
wavfile
pyyaml==5.4.1
pickle

Embedding

Each normalized embedding is created as follows: If you are using our trained model, you may need to use extra parameter Correction(CRR).

Listener Position = LP
Source Position = SP
Room Dimension = RD
Reverberation Time = T60
Correction = CRR

CRR = 0.1 if 0.5<T60<0.6
CRR = 0.2 if T60>0.6
CRR = 0 otherwise

Embedding = ([LP_X,LP_Y,LP_Z,SP_X,SP_Y,SP_Z,RD_X,RD_Y,RD_Z,(T60+CRR)] /5) - 1

Generete RIRs using trained model

Download the trained model using this command

source download_generate.sh

Create normalized embeddings list in pickle format. You can run following command to generate an example embedding list

 python3 example1.py

Run the following command inside code_new to generate RIRs corresponding to the normalized embeddings list. You can find generated RIRs inside code_new/Generated_RIRs

python3 main.py --cfg cfg/RIR_eval.yml --gpu 0

Range

Our trained NN-DAS is capable of generating RIRs with the following range accurately.

Room Dimension X --> 8m to 11m
Room Dimesnion Y --> 6m to 8m
Room Dimension Z --> 2.5m to 3.5m
Listener Position --> Any position within the room
Speaker Position --> Any position within the room
Reverberation time --> 0.2s to 0.7s

Training the Model

Run the following command to download the training dataset we created using a Diffuse Acoustic Simulator. You also can train the model using your dataset.

source download_data.sh

Run the following command to train the model. You can pass what GPUs to be used for training as an input argument. In this example, I am using 2 GPUs.

python3 main.py --cfg cfg/RIR_s1.yml --gpu 0,1

Related Works

1. IR-GAN: Room Impulse Response Generator for Far-field Speech Recognition (INTERSPEECH2021)
2. TS-RIR: Translated synthetic room impulse responses for speech augmentation (IEEE ASRU 2021)

Citations

If you use our FAST-RIR for your research, please consider citing

@INPROCEEDINGS{9747846, 
author={Ratnarajah, Anton and Zhang, Shi-Xiong and Yu, Meng and Tang, Zhenyu and Manocha, Dinesh and Yu, Dong}, 
booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Fast-Rir: Fast Neural Diffuse Room Impulse Response Generator},
year={2022}, 
volume={},
number={},
pages={571-575},
doi={10.1109/ICASSP43922.2022.9747846}}

Our work is inspired by

@inproceedings{han2017stackgan,
Author = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
Title = {StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks},
Year = {2017},
booktitle = {{ICCV}},
}

If you use our training dataset generated using Diffuse Acoustic Simulator in your research, please consider citing

@inproceedings{9052932,
  author={Z. {Tang} and L. {Chen} and B. {Wu} and D. {Yu} and D. {Manocha}},  
  booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},  
  title={Improving Reverberant Speech Training Using Diffuse Acoustic Simulation},   
  year={2020},  
  volume={},  
  number={},  
  pages={6969-6973},
}