| # 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**](https://arxiv.org/pdf/2110.04057.pdf)) for generating room impulse responses (RIRs) for a given rectangular acoustic environment. Our model is inspired by [**StackGAN**](https://github.com/hanzhanggit/StackGAN-Pytorch) architecture. The audio examples and spectrograms of the generated RIRs are available [here](https://anton-jeran.github.io/FRIR/). | |
| **NEWS : We have genaralized our FAST-RIR to generate RIRs for any 3D indoor scenes represented using meshes. Official code of our network [**MESH2IR**](https://anton-jeran.github.io/M2IR/) 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**](https://github.com/GAMMA-UMD/pygsound). 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)**](https://github.com/anton-jeran/IR-GAN) | |
| 2) [**TS-RIR: Translated synthetic room impulse responses for speech augmentation (IEEE ASRU 2021)**](https://github.com/GAMMA-UMD/TS-RIR) | |
| ## 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**](https://github.com/GAMMA-UMD/pygsound) 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}, | |
| } | |
| ``` | |