Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

Last update: Jan 13, 2022

Related tags

Overview

FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating roomimpulse 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.

Requirements

Python3.6
Pytorch
python-dateutil
easydict
pandas
torchfile
gdown
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
   
    <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

Citations

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

@article{ratnarajah2021fast,
  title={FAST-RIR: Fast neural diffuse room impulse response generator},
  author={Ratnarajah, Anton and Zhang, Shi-Xiong and Yu, Meng and Tang, Zhenyu and Manocha, Dinesh and Yu, Dong},
  journal={arXiv preprint arXiv:2110.04057},
  year={2021}
}

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},
}

Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

Related tags

Overview

FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

Requirements

Embedding

Generete RIRs using trained model

Range

Training the Model

Related Works

Citations

Owner

Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"

Convert Table data to approximate values with GUI

Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.

Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

This application explain how we can easily integrate Deepface framework with Python Django application

NeuralForecast is a Python library for time series forecasting with deep learning models

C3DPO - Canonical 3D Pose Networks for Non-rigid Structure From Motion.

Experiment about Deep Person Re-identification with EfficientNet-v2

Unsupervised Foreground Extraction via Deep Region Competition

Posterior temperature optimized Bayesian models for inverse problems in medical imaging

GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

Optimized Gillespie algorithm for simulating Stochastic sPAtial models of Cancer Evolution (OG-SPACE)

RealFormer-Pytorch Implementation of RealFormer using pytorch

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset

[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

NNR conformation conditional and global probabilities estimation and analysis in peptides or proteins fragments

PyTorch implementation of "Simple and Deep Graph Convolutional Networks"

Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

pytorch, hand(object) detect ,yolo v5，手检测

Official project repository for 'Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination'