Neural Network Impedance Analysis for Loudspeaker Verification
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Solution Overview
Problem
The installation of public address systems is time-consuming and prone to errors due to the manual verification of wiring and configuration, which can lead to incorrect loudspeaker settings and potential damage to equipment.
Innovation Solution
An evaluation device utilizing a neural network to analyze the impedance plot of loudspeakers and determine their type, along with additional parameters like temperature and humidity, to quickly and accurately verify the cabling and settings of the public address system, reducing the likelihood of misinterpretation and ensuring correct configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification of wiring and configuration is performed, then the installer can check that the real cabling matches the loaded settings, but the process is time-consuming and highly susceptible to errors
Solution Approach 1:
The system performs self-verification by automatically measuring impedance plots and comparing them against stored reference data for different loudspeaker types. The evaluation device autonomously determines the actual loudspeaker type connected to each amplifier channel without requiring manual verification by the installer, thereby eliminating time consumption while maintaining high reliability through automated comparison and identification processes.
Solution Approach 2:
The manual mechanical process of wiring verification is replaced by an automated electrical measurement system. The evaluation device uses electrical impedance measurements and neural network analysis to automatically identify loudspeaker types and verify cabling configuration, substituting the manual visual and physical verification process with an automated electronic measurement and analysis system that is both faster and more reliable.
2Reliability
If the installer manually loads amplifier channels with loudspeaker settings, then the appropriate limiter settings and filters can be applied, but it cannot be ensured that the correct loudspeaker settings are loaded
Solution Approach 1:
The system implements automatic feedback by measuring the actual impedance plot of connected loudspeakers, comparing it with stored reference data, and automatically identifying the loudspeaker type. Based on this identification, the system automatically loads the correct amplifier channel settings, limiter settings, and filters without requiring manual configuration. This closed-loop feedback mechanism ensures that the correct settings are always applied while simplifying the overall process by eliminating manual intervention.
Solution Approach 2:
The system performs preliminary action by pre-storing impedance plot data and characteristic parameters for multiple loudspeaker types in the evaluation device before installation. During system commissioning, the device automatically retrieves and compares these pre-stored references with actual measurements, enabling immediate and accurate identification and configuration without requiring the installer to manually search for or determine appropriate settings.
3Ease of operation
If wiring verification is performed manually, then the installer can identify incorrect cabling, but incorrect wiring can still result in destruction of loudspeakers
Solution Approach 1:
The system provides beforehand cushioning by automatically verifying the correctness of cabling connections and loudspeaker type identification before allowing the public address system to be activated. The evaluation device measures impedance plots and compares them with reference data to ensure proper configuration, preventing incorrect wiring from causing loudspeaker damage. This preliminary automated verification acts as a protective measure that eliminates the risk of equipment destruction while maintaining operational simplicity.
Data Source
AI summary
The invention relates to an evaluation device (6) for analysing a public address system (1), the public address system (1) comprising at least one loudspeaker (3a-3d) and at least one audio signal output unit (4), the loudspeaker (3a-3d) having an impedance plot (10), the evaluation device (6) comprising a neural network (11), the neural network (11) being trained to determine a loudspeaker type on the basis of an impedance plot (10), the evaluation device (4) being provided with the impedance plot (10) of the loudspeaker (3a-3d) and an additional parameter, and the evaluation device being designed to analyse the loudspeaker (3a-3d) and/or determine the loudspeaker type by means of the neural network (11) on the basis of the provided impedance plot (10) and the additional parameter.