Acoustic Zooming Audio Enhancement via Beamforming
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Solution Overview
Problem
During video playback, audio related to an area of interest can be drowned out by environmental noise, as existing technologies do not effectively enhance audio while visually zooming into specific areas of a video.
Innovation Solution
The system employs a combination of microphones, a camera module, and an acoustic zooming controller that uses beamformers and neural networks to isolate and enhance the audio from a selected area of interest by transforming audio signals into the frequency domain, generating noise-suppressed signals, and combining beamformer signals based on the zoom area's tiles, thereby improving audio clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual zooming into an area of interest is performed during video playback, then the visual clarity of the selected area is improved, but the audio related to that area remains drowned out by environmental noise
Solution Approach 1:
The audio signal is segmented into multiple directional components using beamforming technology. The system divides the audio field into different spatial zones and extracts audio signals from specific directions corresponding to the visually zoomed area, separating target audio from environmental noise through spatial segmentation
Solution Approach 2:
The system applies different audio processing qualities to different spatial regions. The area of interest receives enhanced audio processing with higher gain and noise suppression, while other areas maintain normal audio levels, creating local quality differentiation that matches the visual zoom focus
2Measurement precision
If beamformers and neural networks are used to isolate and enhance audio from a selected area, then audio clarity is improved, but device complexity increases
Solution Approach 1:
The system replaces traditional mechanical audio processing methods with computational approaches. Neural networks and digital signal processing algorithms substitute for physical acoustic filtering, enabling sophisticated audio isolation and enhancement through software-based beamforming and machine learning models
Solution Approach 2:
The system introduces intermediate processing layers including beamformers, neural networks, and audio synthesis components that mediate between the raw microphone signals and the final enhanced audio output. These intermediaries break down the complex task of audio enhancement into manageable processing stages
Data Source
AI summary
Method of performing acoustic zooming starts with microphones capturing acoustic signals associated with video content. Beamformers generate beamformer signals using the acoustic signals. Beamformer signals correspond respectively to tiles of video content. Each of the beamformers is respectively directed to a center of each of the tiles. Target enhanced signal is generated using beamformer signals. Target enhanced signal is associated with a zoom area of video content. Target enhanced signal is generated by identifying the tiles respectively having at least portions that are included in the zoom area, selecting beamformer signals corresponding to identified tiles, and combining selected beamformer signals to generate target enhanced signal. Combining selected beamformer signals may include determining proportions for each of the identified tiles in relation to the zoom area and combining selected beamformer signals based on the proportions to generate the target enhanced signal. Other embodiments are described herein.