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

Related tags

Deep LearningFAST-RIR
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

  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

@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},
}
Owner
GitHub Repository
Exploiting Robust Unsupervised Video Person Re-identification

Exploiting Robust Unsupervised Video Person Re-identification Implementation of the proposed uPMnet. For the preprint, please refer to [Arxiv]. Gettin

1 Apr 09, 2022
NeROIC: Neural Object Capture and Rendering from Online Image Collections

NeROIC: Neural Object Capture and Rendering from Online Image Collections This repository is for the source code for the paper NeROIC: Neural Object C

Snap Research 647 Dec 27, 2022
Graph Representation Learning via Graphical Mutual Information Maximization

GMI (Graphical Mutual Information) Graph Representation Learning via Graphical Mutual Information Maximization (Peng Z, Huang W, Luo M, et al., WWW 20

93 Dec 29, 2022
"SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image", Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image [Paper] [Website] Pipeline Code Environment pip install -r requirements

VITA 250 Jan 05, 2023
A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

SVHNClassifier-PyTorch A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If

Potter Hsu 182 Jan 03, 2023
Implementation of FitVid video prediction model in JAX/Flax.

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper:

Google Research 62 Nov 25, 2022
OpenDILab Multi-Agent Environment

Go-Bigger: Multi-Agent Decision Intelligence Environment GoBigger Doc (中文版) Ongoing 2021.11.13 We are holding a competition —— Go-Bigger: Multi-Agent

OpenDILab 441 Jan 05, 2023
Python Jupyter kernel using Poetry for reproducible notebooks

Poetry Kernel Use per-directory Poetry environments to run Jupyter kernels. No need to install a Jupyter kernel per Python virtual environment! The id

Pathbird 204 Jan 04, 2023
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

LSTMs for Human Activity Recognition Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amon

Guillaume Chevalier 3.1k Dec 30, 2022
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
Wandb-predictions - WANDB Predictions With Python

WANDB API CI/CD Below we capture the CI/CD scenarios that we would expect with o

Anish Shah 6 Oct 07, 2022
WRENCH: Weak supeRvision bENCHmark

🔧 What is it? Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development

Jieyu Zhang 176 Dec 28, 2022
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs SMORE is a a versatile framework that scales multi-hop query emb

Google Research 135 Dec 27, 2022
Storage-optimizer - Identify potintial optimizations on the cloud storage accounts

Storage Optimizer Identify potintial optimizations on the cloud storage accounts

Zaher Mousa 1 Feb 13, 2022
The code repository for EMNLP 2021 paper "Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization".

Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization [Paper] accepted at the EMNLP 2021: Vision Guided Genera

CAiRE 42 Jan 07, 2023
Active Offline Policy Selection With Python

Active Offline Policy Selection This is supporting example code for NeurIPS 2021 paper Active Offline Policy Selection by Ksenia Konyushkova*, Yutian

DeepMind 27 Oct 15, 2022
Implementation of CVPR'2022:Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors (CVPR 2022) Personal Web Pages | Paper | Project Page This repository contains

151 Dec 26, 2022
Universal Probability Distributions with Optimal Transport and Convex Optimization

Sylvester normalizing flows for variational inference Pytorch implementation of Sylvester normalizing flows, based on our paper: Sylvester normalizing

Rianne van den Berg 172 Dec 13, 2022
This repo contains the official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis This repo contains the official implementations of EigenDamage: Structured Prunin

Chaoqi Wang 107 Apr 20, 2022
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

13 Dec 22, 2022