Microphone Placement Model Using Neural Network Optimization
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Solution Overview
Problem
Existing methods for determining microphone placement on devices do not effectively consider sound directionality and intensity, leading to suboptimal speech recognition performance and user convenience, as they fail to account for noise-generating modules within the device during the design stage.
Innovation Solution
A method and apparatus using a neural network algorithm to generate a microphone placement model by minimizing a cost function based on sound feature information, including sound pressure and vibration intensity in multiple directions, to determine the optimal placement of microphones on target devices, considering both internal and external noise sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If microphone placement is determined without considering sound directionality and intensity, then device complexity is reduced, but speech recognition performance deteriorates
Solution Approach 1:
The patent performs preliminary actions by conducting simulated operations during the design stage to obtain sound feature information (sound pressure and vibration intensity) for various microphone placement positions. This advance preparation creates a database of acoustic characteristics that guides optimal microphone and noise-generating module placement, improving speech recognition performance without adding complexity to the actual device operation.
Solution Approach 2:
The patent uses simulated operation to create virtual models and obtain sound feature information that copies real-world acoustic behavior. By analyzing sound pressure and vibration intensity data from simulations, the system determines optimal placement positions without requiring physical prototypes or complex measurement equipment, thus improving reliability while controlling device complexity.
2Object-affected harmful factors
If microphone placement is determined without considering noise-generating modules, then ease of manufacture is improved, but noise interference increases
Solution Approach 1:
The patent extracts and separately analyzes the acoustic impact of noise-generating modules by obtaining sound feature information specifically from these components through simulated operation. By identifying and evaluating noise sources independently, the system can determine microphone placement positions that avoid noise interference, improving acoustic performance while maintaining manufacturing simplicity.
Solution Approach 2:
The patent applies local quality by determining optimal placement positions for microphones based on local sound feature information (sound pressure and vibration intensity) at specific locations within the device. This localized analysis allows the system to identify positions with favorable acoustic characteristics while keeping the overall placement determination process simple and manufacturable.
3Measurement precision
If sound feature information is obtained through simulated operation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs simulated operations to obtain sound feature information during the design stage, which is a preliminary action that prevents the need for time-consuming physical measurements later. By obtaining sound pressure and vibration intensity data through simulations upfront, the system achieves precise acoustic characterization without delaying product development or requiring extensive physical testing.
Data Source
AI summary
Disclosed is an apparatus for determining goodness of fit related to microphone placement capable of communicating with other electronic devices and an external server in a 5G communication network, in which an artificial intelligence (AI) algorithm and/or a machine learning algorithm are executed. The apparatus includes an inputter, a communicator, a storage, and a processor. As the apparatus is provided, sound recognition effects can be improved.