Rec-RIR

Introduction

Official PyTorch implementation of 'Rec-RIR: Monaural Blind Room Impulse Response Identification via DNN-based Reverberant Speech Reconstruction in STFT Domain'

Paper | Code

Room impulse response (RIR) characterizes the complete propagation process of sound in an enclosed space. This paper presents Rec-RIR for monaural blind RIR identification. Rec-RIR is developed based on the convolutive transfer function (CTF) approximation, which models reverberation effect within narrow-band filter banks in the short-time Fourier transform (STFT) domain. Specifically, we propose a deep neural network (DNN) with cross-band and narrow-band blocks to estimate the CTF filter. The DNN is trained through reconstructing the noise-free reverberant speech spectra. This objective enables stable and straightforward supervised training. Subsequently, a pseudo intrusive measurement process is employed to convert the CTF filter estimate into time-domain RIR by simulating a common intrusive RIR measurement procedure. Experimental results demonstrate that Rec-RIR achieves state-of-the-art (SOTA) performance in both RIR identification and acoustic parameter estimation.

Performance

Results

Example

Quick start

Prepare dataset

Follow the guidance in VINP.

Training

# train from scratch
torchrun --standalone --nnodes=1 --nproc_per_node=[number of GPUs] train.py -c config/Rec-RIR.toml -p [saved dirpath]

# resume training
torchrun --standalone --nnodes=1 --nproc_per_node=[number of GPUs] train.py -c config/Rec-RIR.toml -p [saved dirpath] -r 

# use pretrained checkpoints
torchrun --standalone --nnodes=1 --nproc_per_node=[number of GPUs] train.py -c config/Rec-RIR.toml -p [saved dirpath] --start_ckpt ckpt/epoch35.tar

Inference

python inference.py -c config/Rec-RIR.toml --ckpt ckpt/epoch35.tar -i [reverberant speech dirpath] -o [output dirpath]

Citation

If you find our work helpful, please cite

@misc{wang2025recrirmonauralblindroom,
      title={Rec-RIR: Monaural Blind Room Impulse Response Identification via DNN-based Reverberant Speech Reconstruction in STFT Domain}, 
      author={Pengyu Wang and Xiaofei Li},
      year={2025},
      eprint={2509.15628},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2509.15628}, 
}

Please also consider citing our previous work

@ARTICLE{VINP,
  author={Wang, Pengyu and Fang, Ying and Li, Xiaofei},
  journal={IEEE Transactions on Audio, Speech and Language Processing}, 
  title={VINP: Variational Bayesian Inference With Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification}, 
  year={2025},
  volume={33},
  number={},
  pages={4387-4399},
  doi={10.1109/TASLPRO.2025.3622947}}