Neural Audio Localization for Unknown Microphone Arrays
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Solution Overview
Problem
Existing audio processing systems struggle to accurately extract, enhance, or suppress audio signals from specific directions or locations when microphones are not properly calibrated and have unknown configurations, limiting the effectiveness of direction-of-arrival estimation.
Innovation Solution
Utilizing neural networks trained on cross-channel, temporal, and spectral features to generate embeddings or feature vectors that encode location-related information, allowing for location-aware audio processing without requiring knowledge of the microphone configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direction of arrival estimation is performed using a fixed microphone array with known configuration, then audio extraction and enhancement from specific directions is improved, but the system cannot handle random or unknown microphone configurations
Solution Approach 1:
The patent transforms the fixed geometric parameters of microphone arrays into learnable parameters through neural network training. The model learns to map audio signals to spatial locations without requiring explicit knowledge of microphone positions, effectively changing the system from geometry-dependent to data-dependent operation.
Solution Approach 2:
The patent replaces the mechanical/geometric system of fixed microphone arrays with a neural network-based system. Instead of relying on physical microphone configurations and geometric calculations, the system uses learned representations to achieve direction-aware audio processing with unknown or random microphone placements.
2Adaptability or versatility
If microphones are placed in unknown or random configurations, then system deployment flexibility is improved, but direction of arrival estimation becomes uncertain or unknown
Solution Approach 1:
The neural network model performs self-calibration by learning the relationship between audio signals and spatial locations directly from data. The system adapts to the specific microphone configuration it encounters during training without requiring external calibration or known geometric information, making it self-sufficient for unknown configurations.
Solution Approach 2:
The patent performs preliminary training of the neural network model on diverse microphone configurations before deployment. This pre-learning process enables the model to handle unknown configurations during actual use, as the spatial reasoning capabilities are established in advance through exposure to various array geometries.
3Reliability
If traditional direction-based audio processing is used, then audio extraction from known directions is improved, but the system requires calibrated microphone arrays with known geometry
Solution Approach 1:
The patent extracts the essential spatial information from audio signals directly, separating it from the microphone configuration dependencies. By learning to represent spatial locations in a configuration-agnostic manner, the system extracts only the necessary directional cues needed for reliable audio processing without requiring full knowledge of the array geometry.
Data Source
AI summary
Approaches presented herein provide for identification of sound from a sound source relative to an array of microphones of a potentially unknown configuration using, in part, differences in the audio signals received by the microphones. In at least one embodiment, audio signals are captured using an array of microphones and audio features are extracted from those signals. The audio features can be processed using a first neural network to generate a feature vector representing a spatial location of an audio source with respect to the plurality of microphones, where the spatial location is inferred based on audio differences and independent of an availability of information indicating a physical configuration of the plurality of microphones. The feature vector can be provided to a task-specific model to perform at least one audio-related task based in part on the spatial location.