Neural Network Audio Feature Extraction for Spatial Localization
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Solution Overview
Problem
Current spatial audio rendering technologies in virtual and augmented reality struggle to accurately personalize sound localization cues for individual users due to deviations from generic head-related impulse responses (HRIRs), leading to a degraded listening experience.
Innovation Solution
A neural network-based approach, specifically using convolutional neural networks (CNNs) with layer-wise relevance propagation (LRP), is employed to identify and personalize audio features for accurate sound source localization, focusing on elevation cues by training models on diverse HRTF datasets to account for individual anthropometric variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic head-related impulse responses (HRIRs) are used for spatial audio rendering, then device complexity is reduced, but measurement precision and localization accuracy deteriorate due to deviations from individual user anthropometric features
Solution Approach 1:
The system performs preliminary measurement of individual user HRIRs and extracts anthropometric features before spatial audio rendering. This allows the system to prepare personalized acoustic models in advance, resolving the contradiction by enabling accurate localization through pre-captured individual characteristics while maintaining computational efficiency during actual rendering operations
Solution Approach 2:
The system changes physical parameters by measuring actual HRIRs for each user and extracting anthropometric parameters (head size, ear shape, etc.). These parameter changes enable the system to adapt generic rendering algorithms to individual users, improving localization accuracy without requiring completely new rendering systems
2Measurement precision
If individualized HRIR measurement is performed for each user, then sound localization accuracy is improved, but device complexity and measurement time increase
Solution Approach 1:
The system extracts only the essential anthropometric features from complete HRIR measurements, such as head-related transfer function parameters and key spectral characteristics. This extraction process reduces the data complexity while preserving the information necessary for accurate localization, allowing individualized rendering without storing or processing complete HRIR datasets
Solution Approach 2:
The system creates simplified copies of individual HRIR characteristics through anthropometric feature representations. Instead of using complete individualized HRIRs, the system generates parameter-based models that replicate the essential localization cues, reducing system complexity while maintaining localization accuracy
3Measurement precision
If complete HRIR datasets are stored for each user, then localization accuracy is improved, but data storage requirements and processing time increase
Solution Approach 1:
The system extracts and stores only critical anthropometric parameters from complete HRIR measurements, such as head radius, pinna dimensions, and spectral notch frequencies. This selective extraction reduces storage requirements and accelerates processing by focusing only on the parameters that most significantly impact localization accuracy
Solution Approach 2:
The system performs complete HRIR measurements and feature extraction in advance during a calibration phase, storing only the extracted parameters for later use. This preliminary action separates the time-consuming measurement process from the real-time rendering process, improving processing speed during actual spatial audio operations
Data Source
AI summary
Systems, methods, and computer-readable storage devices are disclosed for improved audio feature discovery using a neural network. One method including: receiving a trained neural network model, the trained neural network configured to output an audio feature classification of audio data; deconstructing the trained neural network model to generate at least one saliency map, the at least one saliency map providing a successful classification of the audio feature; and extracting at least one visualization of the audio feature the trained neural network model relies on for classification based on the at least one saliency map.