DeepRIRNet: A Room Impulse Response Prediction Framework Based on Deep Recurrent Neural Networks and Physical Constraints

DeepRIRNet is an acoustic modeling framework implemented in PyTorch. It combines deep recurrent neural networks with physically inspired regularization losses to generate and predict Room Impulse Responses (RIRs), and supports transfer learning to quickly adapt to new acoustic environments. The project is maintained by ShahabP and open-sourced on GitHub (link: https://github.com/ShahabP/DeepRIRnet), with a release date of June 16, 2026.

Room Impulse Response (RIR) describes the complete response of a room to sound, including geometry, materials, and the relationship between sound source and microphone positions. It is the foundation for applications such as virtual acoustics and spatial audio. Traditional acquisition methods: on-site measurement is costly and lacks flexibility; physical simulation has high computational overhead. Deep learning-driven methods have become a hot topic, and DeepRIRNet is an exploration in this direction.

The framework uses an encoder-decoder architecture:

• Geometric Encoder: Maps room dimensions, absorption coefficients, and 3D positions of sound sources/microphones to a latent space;
• Temporal Decoder: Uses multi-layer LSTM with residual connections and layer normalization to generate RIR temporal signals point by point;
• Output Layer: Obtains the final RIR sample values through linear projection. This architecture captures both spatial geometric features and temporal dynamic characteristics.

In addition to traditional MSE and log spectral distance reconstruction losses, two physical regularization terms are introduced:

1. Sparsity Regularization: Encourages the generation of sparse RIRs with energy concentrated in early reflections and later attenuation;
2. Energy Decay Regularization: Enforces compliance with the physical law that RIR energy decays exponentially over time. The integration of physical information improves prediction quality, interpretability, and generalization ability.

DeepRIRNet supports transfer learning to quickly adapt to new environments:

1. Source Domain Pre-training: Learns general acoustic features on a large dataset of standard rectangular rooms;
2. Layer Freezing Strategy: Freezes parameters of early LSTM layers to retain general feature extraction capabilities;
3. Target Domain Fine-tuning: Uses a small dataset to fine-tune the model to adapt to specific room characteristics. This mechanism reduces target domain data dependency and accelerates deployment.

• Input Features: 10-dimensional structured features (3D room dimensions, 1D absorption coefficient, 3D sound source position, 3D microphone position);
• Configuration Management: Hyperparameters are centrally managed via config.py (model architecture, training, data configuration);
• Code Quality: Uses type annotations, complete documentation, and standardized package structure, facilitating secondary development and reproduction.

DeepRIRNet application scenarios include:

• Virtual acoustics (realistic spatial audio for games, VR/AR);
• Speech enhancement (assisting echo cancellation, dereverberation);
• Architectural acoustics (predicting acoustic performance during the design phase);
• Audio production (providing virtual acoustic environments). The framework provides a reference for the application of physics-informed neural networks in the acoustic field.

DeepRIRNet combines deep recurrent neural networks with physical constraints to provide an efficient and physically consistent RIR prediction solution, and its transfer learning capability enhances its practical value. It is recommended that developers and researchers engaged in spatial audio, virtual acoustics, or physics-informed neural network research pay attention to and try this open-source project.