Binaural Audio Emulation via Neural Network
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Solution Overview
Problem
Current binaural audio applications are limited as they require known microphone positions and specialized equipment, making them impractical for consumer devices and unable to effectively generate immersive audio in complex environments with multiple sources.
Innovation Solution
A binaural audio emulation system using a neural network that converts non-binaural audio signals from multiple microphones into binaural audio signals, allowing for immersive audio generation on consumer devices without the need for expensive equipment or prior knowledge of microphone positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized binaural recording equipment is used, then binaural audio quality is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a neural network to learn and copy the complex binaural recording characteristics from training data recorded with specialized equipment. The network model replicates the functionality of expensive binaural microphones and HRTF measurement systems, enabling standard microphones to generate binaural audio without physically copying the specialized equipment.
Solution Approach 2:
The patent replaces the mechanical/physical binaural recording system (specialized microphones positioned at ear locations, HRTF measurement setups) with a computational neural network system. The neural network processes audio signals algorithmically to produce binaural output, substituting physical measurement mechanisms with computational modeling.
2Measurement precision
If binaural recording equipment is used, then audio localization accuracy is improved, but cost increases
Solution Approach 1:
The patent enables inexpensive standard microphones to perform binaural audio recording through neural network processing. Instead of requiring expensive, specialized binaural microphones, the system uses readily available, low-cost microphones combined with computational processing to achieve the same localization accuracy.
Solution Approach 2:
The neural network is trained on data from expensive binaural equipment and learns to replicate its performance. This allows the system to copy the high-quality localization capabilities of expensive equipment using inexpensive hardware combined with learned computational models.
3Measurement precision
If microphone positions are known beforehand, then binaural audio generation is improved, but adaptability decreases
Solution Approach 1:
The patent makes the system adaptive to different microphone configurations through neural network training. Instead of requiring fixed, pre-determined microphone positions, the network can be trained on various configurations and adaptively process audio from different microphone arrangements, making the system dynamic and flexible rather than static and rigid.
Solution Approach 2:
The patent changes the approach from requiring fixed microphone position parameters to using neural network training that can accommodate varying microphone configurations. The system adapts to different spatial arrangements, sampling rates, and microphone types by learning from diverse training data, making parameters flexible rather than fixed.
Data Source
AI summary
A system, article, device, apparatus, and method of binaural audio emulation comprises receiving, by processor circuitry, multiple audio signals from multiple microphones and overlapping in a same time and associated with a same at least one audio source. The method also comprises generating binaural audio signals comprising inputting at least one version of the multiple audio signals into a neural network.