Spatial Procedural Audio with Machine Learning for Realistic XR Sound
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Solution Overview
Problem
Traditional procedural audio techniques rely on physical characteristics and semi-empirical models, often failing to produce realistic audio with sufficient uniqueness and realism, especially in extended reality environments.
Innovation Solution
A method using a machine learning model, such as a Generative Adversarial Network (GAN), generates spatial procedural audio by processing noise signals to produce unique and realistic audio effects without relying on image data, incorporating spatial parameters like Direction of Arrival and diffuseness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional procedural audio techniques using physical characteristics and semi-empirical models are used, then the system can generate procedural audio, but the audio lacks sufficient realism and uniqueness
Solution Approach 1:
The patent replaces traditional mechanical/physical modeling approaches (semi-empirical models of objects) with a machine learning model (deep neural network). This substitution allows the system to learn realistic audio generation patterns from training data without relying on complex physical models, thereby improving realism while reducing system complexity.
Solution Approach 2:
The patent changes the fundamental parameters of the audio generation system by transitioning from fixed physical models to a data-driven neural network model with learnable parameters. The model takes noise signals as input and generates audio signals with spatial characteristics, allowing dynamic adjustment of audio properties without complex physical simulations.
2Adaptability or versatility
If traditional procedural audio techniques relying on image data and empirical models are used, then spatial audio can be generated, but the system requires multiple input sources and complex processing
Solution Approach 1:
The patent creates a universal audio generation model that can handle multiple audio scenarios and spatial configurations through a single deep neural network. The model is trained on diverse spatial audio data and can generate different types of sounds with appropriate spatial characteristics, eliminating the need for separate processing pipelines for different input sources.
Solution Approach 2:
The patent extracts and processes only the essential audio signal and spatial parameters through the neural network, removing the dependency on image data and other non-audio inputs. The model directly maps noise signals to spatial audio outputs, simplifying the input requirements while maintaining spatial audio generation capabilities.
3Reliability
If procedural audio is generated using pre-recorded libraries, then consistent audio quality can be achieved, but the audio lacks uniqueness for different situations
Solution Approach 1:
The patent introduces dynamic audio generation where the deep neural network processes different noise signals to generate unique audio outputs for each situation. The model maintains consistent quality through its trained parameters while adapting to different contexts by varying its input noise signals, enabling both consistency and uniqueness simultaneously.
Solution Approach 2:
Instead of using fixed pre-recorded copies from libraries, the patent uses the neural network to synthesize new audio signals that replicate the characteristics of real-world sounds. The model learns from training data and generates novel audio instances that maintain quality consistency while being unique to each generation event.
Data Source
AI summary
A method performed by a programmed processor, the method including receiving an input noise signal, generating, using a machine learning model that has an input based on the input noise signal, a mono audio signal that includes a sound and a spatial parameter for the mono audio signal, and generating spatial audio data by spatially encoding the mono audio signal according to the spatial parameter.