Sound Input Output Device Inspection via Cross-Correlation Analysis
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Solution Overview
Problem
Current methods for inspecting sound input/output devices lack reliability and efficiency in monitoring their performance, particularly in maintaining appropriate performance for speech recognition and synthesis tasks.
Innovation Solution
A method using deep learning to inspect sound input/output devices by outputting sound signals, receiving feedback signals, and analyzing the correlation between the two spectrums to detect error states, including calculating cross-correlation coefficients and determining error states based on predetermined thresholds and noise levels, with the option to adjust the inspection signal and environment for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional inspection methods are used for sound input/output devices, then the inspection process is simple, but the reliability of inspection results is low
Solution Approach 1:
The patent implements a feedback mechanism where the sound output device outputs a test sound signal, the sound input device receives it, and the system compares the original signal with the received signal to detect errors. This closed-loop feedback process significantly improves inspection reliability by enabling continuous monitoring and comparison of signal integrity throughout the sound transmission path.
Solution Approach 2:
The patent replaces traditional mechanical or manual inspection methods with signal processing and spectral analysis. By using frequency domain transformation (FFT) and cross-correlation coefficient calculation, the system automatically detects errors in sound input/output devices, substituting complex manual testing with automated digital signal processing techniques.
2Measurement precision
If spectral analysis and cross-correlation methods are used, then the accuracy of error detection is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining threshold values for cross-correlation coefficients and preparing frequency transformation parameters before actual inspection. This allows the system to quickly compare actual measurements against pre-established criteria, reducing real-time computational complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent transforms the sound signals from time domain to frequency domain using Fast Fourier Transform (FFT), changing the parameter representation to enable more effective error detection. By analyzing spectral characteristics and frequency components, the system achieves higher measurement precision in detecting sound device errors while the transformation itself becomes a standardized computational step.
3Reliability
If the inspection is performed continuously, then the monitoring reliability is improved, but the energy consumption increases
Solution Approach 1:
The patent implements periodic inspection rather than continuous monitoring, where test sound signals are output at predetermined intervals. This periodic action maintains monitoring reliability by regularly checking sound device performance while significantly reducing energy consumption compared to continuous operation, as the inspection process is activated only when needed.
Data Source
AI summary
A method of inspecting a sound input/output device is disclosed. A method of inspecting a sound input/output device according to an embodiment of the present disclosure can diagnose an error state of either a speaker or a microphone based on a cross-correlation of input/output signals by receiving a sound signal from an AI device through the microphone. The method of inspecting of the present disclosure may be associated with an artificial intelligence module, a drone ((Unmanned Aerial Vehicle, UAV), a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a device associated with 5G services, etc.