Neural Network Audio Unifier for Microphone Switching Consistency
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Solution Overview
Problem
Conventional methods fail to seamlessly switch between different microphones during audio conferences, causing noticeable changes in audio signals due to variations in acoustic environments, microphone types, and recording parameters, leading to distracting and annoying sound quality for users.
Innovation Solution
A neural network-based platform sound unifier system that modifies initial audio signals from multiple microphones to generate unified audio signals with characteristics closer to a generic audio signal, reducing perceptible differences and improving sound quality regardless of the microphone type or environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users switch between different microphone types for convenience, then ease of operation is improved, but audio signal consistency deteriorates causing distracting sound quality changes
Solution Approach 1:
The patent introduces a neural network-based audio signal processing system as an intermediary between the microphone and the audio output. This mediator analyzes the audio signal characteristics and dynamically adjusts parameters to maintain consistency across different microphone types, allowing users to switch microphones freely without perceptible quality changes.
Solution Approach 2:
The system dynamically changes audio processing parameters such as gain, equalization, and noise reduction settings based on the detected microphone type and acoustic environment. By adjusting these parameters in real-time, the system maintains consistent audio output quality regardless of which microphone is currently in use.
2Reliability
If the system processes audio signals to unify microphone characteristics, then audio signal consistency is improved, but device complexity increases due to neural network processing
Solution Approach 1:
The system performs preliminary classification of the microphone type and acoustic environment before applying the full neural network processing. This preliminary action allows the system to select from pre-configured processing profiles, reducing the computational complexity required during real-time audio processing while maintaining consistency.
Solution Approach 2:
The audio processing system is segmented into multiple independent modules: microphone type detection, acoustic environment analysis, neural network processing, and output adjustment. This segmentation allows each module to be optimized independently and enables the system to bypass certain processing stages when full processing is not required, reducing overall complexity.
Data Source
AI summary
A system, article, device, apparatus, and method for a multi-microphone audio signal unifier comprises receiving, by processor circuitry, an initial audio signal from one of multiple microphones arranged to provide the initial audio signal. This also includes modifying the initial audio signal comprising using at least one neural network (NN) to generate a unified audio signal that is more generic to a type of microphone than the initial audio signal.