Hybrid Neural Network Audio Capture with Pre-computed Beamforming Weights
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Solution Overview
Problem
Traditional audio systems with microphone arrays face inefficiencies in capturing dynamic audio sources due to the need for re-steering beamforming lobes, which consumes computational resources, introduces noise, and results in undesirable audio delays and non-audio capture areas.
Innovation Solution
A hybrid neural network model integrates machine learning with digital signal processing to predict audio immersion by transforming audio stream inputs into feature sets and generating augmented signal path data vectors, providing improved audio quality and noise reduction across both audio and non-audio capture areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beamforming lobes are re-steered to track dynamic audio sources, then audio capture accuracy is improved, but computational resource consumption increases and audio delays are introduced
Solution Approach 1:
The system pre-calculates and stores beamforming weight sets for multiple possible source locations before they are needed. When an audio source is detected at a new location, the pre-computed weight set corresponding to that location is immediately applied without real-time calculation, thereby eliminating computational delay while maintaining accurate audio capture.
Solution Approach 2:
The system dynamically switches between pre-computed beamforming weight sets based on the detected location of the audio source. This dynamic adaptation allows the system to track moving sources efficiently by selecting from pre-prepared configurations rather than recalculating weights continuously, reducing computational overhead and delay.
2Measurement precision
If beamforming lobes are re-steered to track dynamic audio sources, then audio capture accuracy is improved, but computational complexity increases
Solution Approach 1:
The system pre-calculates and stores beamforming weight sets for multiple possible source locations before they are needed. When an audio source is detected at a new location, the pre-computed weight set corresponding to that location is immediately applied without real-time calculation, thereby eliminating computational delay while maintaining accurate audio capture.
Solution Approach 2:
The system creates and stores copies of beamforming weight sets for different source locations. Instead of performing complex real-time calculations, the system simply selects and applies the appropriate pre-computed weight copy corresponding to the current source location, significantly reducing computational complexity during operation.
3Device complexity
If traditional microphone arrays are used with fixed positions, then device complexity is reduced, but audio capture coverage creates non-audio capture areas
Solution Approach 1:
The system dynamically switches between pre-computed beamforming weight sets based on the detected location of the audio source. This dynamic adaptation allows the system to track moving sources efficiently by selecting from pre-prepared configurations rather than recalculating weights continuously, reducing computational overhead and delay.
Solution Approach 2:
The system uses a single microphone array that can adapt to multiple source locations through beamforming weight switching. This multi-functional capability allows the fixed microphone array to effectively capture audio from various positions in the environment, providing wide coverage without adding more physical microphones or increasing device complexity.
Data Source
AI summary
Techniques for predicted audio immersion related to audio capture devices within an audio environment are discussed herein. Examples may include receiving audio stream inputs associated with audio capture devices positioned within one or more audio capture areas of an audio environment, where the audio environment comprises the one or more audio capture areas and one or more non-audio capture areas. Additionally, the audio stream inputs are transformed into respective audio feature sets. The respective audio feature sets are then input to a hybrid neural network model configured to generate respective augmented signal path data vectors for the respective audio stream inputs. Respective neural network paths of the hybrid neural network model may process one or more of the respective audio feature sets based on respective digital signal processing augmentation networks integrated within the hybrid neural network model.