Spatialized Audio IIR Filtering via Modal Impulse Interpolation
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Solution Overview
Problem
Existing spatialized audio technologies face challenges in creating immersive experiences due to the computational complexity and memory requirements of finite impulse response (FIR) filters, and neural networks struggle to learn optimal HRTF interpolation, resulting in sub-optimal quality and practicality issues.
Innovation Solution
A neural network is trained to interpolate the modal components of an impulse response, which are used to determine coefficients for an infinite impulse response (IIR) filter, transforming anechoic audio into spatialized audio with fewer coefficients, reducing computational complexity and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If FIR filters are used to transform anechoic audio into spatialized audio, then spatialized audio quality is achieved, but computational complexity and memory usage increase substantially
Solution Approach 1:
The patent changes the fundamental parameters of the filter implementation by transitioning from FIR (finite impulse response) to IIR (infinite impulse response) filters. This parameter change allows the system to achieve similar spatialized audio quality with significantly reduced computational complexity and memory requirements, as IIR filters require fewer coefficients to represent the same impulse response characteristics
Solution Approach 2:
The patent uses neural networks to learn and copy the essential characteristics of HRTF impulse responses rather than directly implementing the full FIR filters. The neural network captures the key temporal and spectral features of the impulse responses and reproduces them through a compact IIR filter structure, achieving a efficient approximation that maintains audio quality while reducing computational burden
2Adaptability or versatility
If neural networks are trained to interpolate HRTF magnitude response, then spatialized audio can be generated at arbitrary directions, but the network struggles to learn optimal interpolation resulting in sub-optimal quality
Solution Approach 1:
The patent transforms the interpolation problem from the frequency domain to the time domain by training the neural network to predict impulse response characteristics rather than magnitude response. This dimensional change allows the network to learn temporal patterns and interpolations more effectively, capturing the dynamic behavior of HRTFs across different directions with better accuracy
Solution Approach 2:
The patent replaces the traditional approach of directly interpolating magnitude response with a neural network-based system that learns impulse response characteristics. This substitution enables the system to capture complex temporal relationships and non-linear patterns that are difficult to model through direct magnitude interpolation, resulting in superior spatialized audio quality at arbitrary directions
3Measurement precision
If FIR filters with many coefficients are used, then accurate impulse response representation is achieved, but practicality and ease of implementation decrease
Solution Approach 1:
The patent extracts and identifies the most critical characteristics of the impulse response that contribute to spatialized audio quality. By focusing on these essential features rather than representing the complete impulse response with numerous FIR coefficients, the system achieves accurate representation with a compact IIR filter structure, improving practicality while maintaining precision
Solution Approach 2:
The patent implements a partial representation of the impulse response by using IIR filters that capture the dominant temporal and spectral characteristics without requiring complete fidelity to the original impulse response. This partial action approach achieves sufficient accuracy for high-quality spatialized audio while dramatically reducing the number of parameters needed, enhancing ease of implementation
Data Source
AI summary
Systems, methods, software, and devices are disclosed herein that transform spatial input into modal output comprising learned modal components of an impulse response. A neural network interpolates the modal components of the impulse response based on a desired sound source direction represented in the spatial input. The learned modal components are then used to determine coefficients for an infinite impulse response filter that transforms anechoic audio into spatialized audio. The spatialized audio provides a directional effect to a listener as having arrived from the desired sound source direction.