AI Acoustic Equalization for User Location and Room Response
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Solution Overview
Problem
Conventional acoustic equalization methods fail to account for user location and space characteristics, resulting in unsatisfactory acoustic signal transmission in various environments.
Innovation Solution
A method utilizing a robot and AI server that inputs space and user location information into an artificial neural network-based algorithm model to calculate an equalization value, incorporating a speaker, microphone, camera, and sensing unit to optimize acoustic signal delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional equalization methods are used, then the equalization process is simple, but the acoustic signal transmission quality is unsatisfactory because user location and space characteristics are not reflected
Solution Approach 1:
The system performs preliminary actions by collecting space information (size, shape, objects) and user location information before equalization, then uses this pre-collected data as input to the neural network model to calculate optimal equalization values, ensuring high-quality acoustic transmission without complex real-time processing
Solution Approach 2:
An artificial neural network-based algorithm model serves as an intermediary between the collected space/user data and the equalization process. The model automatically processes the input data and generates equalization values, eliminating the need for complex manual equalization while ensuring optimal acoustic signal transmission
2Adaptability or versatility
If the robot moves to different locations, then the user can be served at multiple positions, but the acoustic equalization must be recalculated for each new location
Solution Approach 1:
When the robot is at a new location, space information and user location information are collected in advance before the equalization process begins. This preliminary data collection enables the neural network model to quickly calculate equalization values without time-consuming real-time processing during actual operation
Solution Approach 2:
The system implements a feedback mechanism where the robot continuously obtains user location information and space characteristics, feeds this data back to the neural network model, and receives updated equalization values. This closed-loop feedback enables rapid adaptation to new locations while maintaining optimal acoustic transmission quality
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables optimal acoustic signal reception for users regardless of their location within a space, improving acoustic signal quality across different environments.
Implementation Method 1
a speaker outputting a first acoustic signal for equalizing acoustic at a first point in a space; a microphone receiving the first acoustic signal reflected in the space at the first point
Data Source
AI summary
A acoustic equalization method, and a robot and an AI server implementing the same are disclosed. The robot inputs space information with respect to a certain space and location information of a user to an artificial neutral network-based algorithm model to calculate an equalization value with respect to the certain space. Here, the space information is calculated based on at least one of a first acoustic signal output and received at a first point in the space, an image acquired through a camera, and distance information related to a space sensed through a sensing unit.