Audio Signal Processing System for Impulse Response Simulation
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Solution Overview
Problem
Current methods for obtaining room impulse responses are costly and inefficient, requiring live recordings that are difficult to scale and cover various environments, and existing simulations are computationally complex and limited to specific room types.
Innovation Solution
An audio signal processing method that simulates impulse responses by determining scenario layout parameters, sampling audio signals, calculating simulated traveling distances and reflections, and generating impulse responses based on reflection losses, without the need for complex physical modeling or specialized computing platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If live recording methods are used to obtain impulse responses, then the accuracy and realism of the impulse responses are improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world acoustic environments through simulated impulse responses generated by neural networks. Instead of physically recording in each target environment, the system learns from a small set of real recordings and generates accurate simulations of impulse responses for various environments, furnishings, and configurations, dramatically reducing field recording requirements
Solution Approach 2:
The system performs preliminary training by collecting impulse responses from a limited set of real environments beforehand. This pre-trained knowledge is then applied to generate impulse responses for any target environment without requiring actual field recording at the target location, saving significant time and resources
2Reliability
If a large quantity of real impulse response data is collected to improve model accuracy, then the training data quality is improved, but the cost and complexity of data collection increase
Solution Approach 1:
The system generates large quantities of training data by creating virtual copies and variations of real impulse responses. The neural network learns from a small set of authentic recordings and synthesizes additional training examples through parameter variations, eliminating the need for extensive field data collection while maintaining model accuracy
Solution Approach 2:
The system varies parameters such as environment type, furnishings, audio source positions, and receiver positions to generate diverse training examples from a limited set of base recordings. This parameter-based generation creates realistic variations without requiring physical re-recording for each scenario
3Measurement precision
If complex physical modeling is used to simulate impulse responses, then the simulation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent replaces complex mechanical/physical simulation models with a neural network-based approach. Instead of solving differential equations or performing ray tracing to model acoustic wave propagation, the system uses a trained neural network that directly maps input parameters to impulse responses, dramatically reducing computational complexity while maintaining accuracy
Solution Approach 2:
The neural network learns the complex physical relationships from training data and creates virtual copies of the acoustic behavior. Once trained, the network efficiently generates impulse responses by copying the learned patterns rather than重新计算 physical parameters, achieving both accuracy and computational efficiency
4Ease of manufacture
If existing simulation methods are used to generate impulse responses, then the cost is reduced, but the adaptability to different environments and furnishings is limited
Solution Approach 1:
The neural network is designed as a universal system that can handle multiple environment types, furnishings, and configurations through a single model. By training on diverse data and using parameter-based input, the system adapts to various scenarios without requiring separate simulation models for each environment type, achieving both low cost and high versatility
Solution Approach 2:
The system achieves adaptability through parameter variation rather than model modification. By changing input parameters such as environment type, furnishings, and positions, the same neural network generates appropriate impulse responses for different scenarios, providing unlimited adaptability without increasing system complexity or cost
Data Source
AI summary
This application relates to an audio signal processing method performed by a computer device. The method includes: obtaining a set of scenario layout parameters corresponding to a current simulated scenario; sampling, at a preset sampling rate, an audio signal emitted by at least one audio source to obtain at least one sample; determining, based on a linear distance, a simulated traveling distance corresponding to each sample; determining a number of simulated reflections based on the simulated traveling distance; determining a reflection coefficient based on an environmental-spatial parameter, and respectively determining, based on the reflection coefficient, the simulated traveling distance, and the number of simulated reflections, a simulated reflection loss corresponding to each audio source; and generating a simulated impulse response under the current simulated scenario based on the simulated reflection loss corresponding to each audio source.