Neural Network Impulse Response Generation for Real-Time Audio
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Solution Overview
Problem
Generating high fidelity impulse responses in real time for virtual environments is challenging due to resource and time-consuming wave-based solvers, especially in scenarios requiring seamless movement and high audio fidelity, such as video game environments.
Innovation Solution
A method using a neural network architecture that generates impulse responses by training an autoencoder and IR neural network with simulated impulse responses, allowing for fast and efficient generation of high fidelity reverberating audio signals by convolving anechoic audio signals with the generated impulse responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wave-based solvers are used to generate impulse responses, then high fidelity audio rendering is achieved, but computational resources and time consumption increase significantly
Solution Approach 1:
The system pre-generates impulse responses at multiple predetermined listening positions using wave-based solvers before the user actually needs them. These pre-computed impulse responses are stored in memory, allowing the system to switch between them instantly during real-time audio rendering without performing heavy wave-based calculations at runtime.
Solution Approach 2:
Instead of computing unique impulse responses for every possible listening position, the system creates copies of impulse responses for a limited set of predetermined positions. During runtime, the system selects and uses these pre-computed copies based on the user's current position, avoiding the need to generate new impulse responses continuously.
2Measurement precision
If wave-based solvers are used to simulate impulse responses for every listening position, then high fidelity is maintained, but real-time audio feedback becomes difficult to obtain
Solution Approach 1:
The system performs wave-based impulse response generation in advance at predetermined listening positions and stores these results. When the user moves to a new position, the system quickly selects the pre-computed impulse response from the nearest predetermined position rather than performing time-consuming wave-based calculations in real-time.
Solution Approach 2:
The system dynamically selects from a set of pre-computed impulse responses based on the user's current listening position. By associating each predetermined position with its pre-computed impulse response and selecting the appropriate one during runtime, the system adapts to user movement without requiring real-time wave-based computation.
Data Source
AI summary
A method for generating an impulse response representing a sound wave propagation from at least one sound source received at a listening point in a room includes obtaining the generated impulse response at the listening point in the room from a neural network architecture by providing at least the position of the listening point as input. The generated impulse response is generated using a neural network architecture. The network is trained by obtaining a 3D model of the room including the at least one sound source emitting sound in the room and obtaining a training group of simulated impulse responses, wherein each simulated impulse response is generated for a respective predefined listening point in the 3D model of the virtual room. An autoencoder is trained by training an encoder of the autoencoder by using the training group of simulated impulse responses as input in order to obtain a corresponding training group of compressed simulated impulse response as outputs and training a decoder of the autoencoder by using the training group of compressed impulse responses as input in order to obtain a corresponding training group of uncompressed simulated impulse response as outputs. An IR neural network is trained using the training group of compressed simulated impulse responses of the autoencoder and the corresponding position of the predefined listening points as input.