ABSTRACT Physically based modal sound synthesis generates realistic audio by modeling structural vibration and acoustic radiation from object geometry and material properties. However, traditional approaches rely on computationally expensive eigenvalue analysis and acoustic transfer simulations, making them impractical for real‐time virtual reality (VR), especially for dynamically generated or fractured objects. We present NeuroSonic, a unified neural framework for real‐time physically based sound synthesis directly from point cloud geometry. Our key idea is to amortize modal analysis and acoustic transfer using neural networks, replacing costly numerical solvers with efficient feed‐forward inference. The framework predicts modal eigenvalues, eigenvectors, and acoustic transfer functions, forming a physically grounded representation for sound synthesis. By operating on point clouds, our method avoids voxelization artifacts and handles geometrically complex and thin structures. Once trained, NeuroSonic computes complete modal and acoustic information in under 0.02 s, enabling real‐time performance. Experiments show that our approach achieves accuracy comparable to physically based solvers while providing orders‐of‐magnitude speedup, making it a practical solution for interactive VR environments.

Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this challenge, we present Neural Acoustic Transfer, a novel approach that leverages implicit neural representations to encode acoustic transfer functions and their variations. This enables real-time prediction of dynamically evolving sound fields and their interactions with the environment under varying conditions. To efficiently generate high-quality training data for the neural acoustic field while avoiding reliance on mesh quality of a model, we develop a fast and efficient Monte-Carlo-based boundary element method (BEM) approximation, suitable for general scenarios with smooth Neumann boundary conditions. In addition, we devise strategies to mitigate potential singularities during the synthesis of training data, thereby enhancing its reliability. Together, these methods provide robust and accurate data that empower the neural network to effectively model complex sound radiation space. We demonstrate our method's numerical accuracy and runtime efficiency (within several milliseconds for 30 s audio) through comprehensive validation and comparisons in diverse acoustic transfer scenarios. Our approach allows for efficient and accurate modeling of sound behavior in dynamically changing environments, which can benefit a wide range of interactive applications such as virtual reality, augmented reality, and advanced audio production.

We present a novel learning-based modal sound synthesis approach that includes a mixed vibration solver for modal analysis and a radiation network for acoustic transfer. Our mixed vibration solver consists of a 3D sparse convolution network and a Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) module for iterative optimization. Moreover, we highlight the correlation between a standard numerical vibration solver and our network architecture. Our radiation network predicts the Far-Field Acoustic Transfer maps (FFAT Maps) from the surface vibration of the object. The overall running time of our learning-based approach for most new objects is less than one second on a RTX 3080 Ti GPU while maintaining a high sound quality close to the ground truth solved by standard numerical methods. We also evaluate the numerical and perceptual accuracy of our approach on different objects with various shapes and materials.

Model sound synthesis is a physically-based sound synthesis method used to generate audio content in games and virtual worlds. We present a novel learning-based impact sound synthesis algorithm called Deep-Modal. Our approach can handle sound synthesis for common arbitrary objects, especially dynamic generated objects, in real-time. We present a new compact strategy to represent the mode data, corresponding to frequency and amplitude, as fixed-length vectors. This is combined with a new network architecture that can convert shape features of 3D objects into mode data. Our network is based on an encoder-decoder architecture with the contact positions of objects and external forces embedded. Our method can synthesize interactive sounds related to objects of various shapes at any contact position, as well as objects of different materials and sizes. The synthesis process only takes ~0.01s on a GTX 1080 Ti GPU. We show the effectiveness of Deep-Modal through extensive evaluation using different metrics, including recall and precision of prediction, sound spectrogram, and a user study.