Signal Processing Apparatus for Noise Suppression and Signal Restoration
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Solution Overview
Problem
Conventional signal processing technologies struggle to effectively separate necessary signal components from unnecessary components like noise, especially in industrial applications where noise levels are high and necessary signals are sparse.
Innovation Solution
A signal processing apparatus that includes a signal processing unit for restoring the necessary signal component from an input signal using learned parameters, a first signal adjustment unit for multiplying the signals by a weighting coefficient to correct errors, and an update unit for optimizing the parameters based on the corrected signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional signal processing is used to suppress unnecessary components like noise, then noise suppression is improved, but necessary signal components are also suppressed
Solution Approach 1:
The system performs preliminary action by generating a restoration signal before final signal processing using predictive models that anticipate the original signal characteristics. This allows the necessary signal components to be preserved before noise suppression is applied, preventing their loss.
Solution Approach 2:
The system implements feedback mechanisms where the restored signal is continuously compared with the input signal, and the difference (error signal) is fed back to adjust the restoration process. This feedback loop ensures that necessary signal components are maintained while noise is suppressed, as the system learns from its own performance.
2Object-affected harmful factors
If signal processing is performed to reduce noise in industrial applications, then noise levels are reduced, but signal restoration accuracy deteriorates due to low signal-to-noise ratios
Solution Approach 1:
The system generates a restoration signal in advance using predictive models trained on signal characteristics, allowing the necessary components to be recovered before noise suppression is applied. This preliminary restoration action maintains accuracy even in low signal-to-noise ratio conditions.
Solution Approach 2:
The system changes parameters by using learned models that adapt to different signal characteristics and noise conditions. By adjusting the restoration parameters based on the specific input signal properties, the system maintains high restoration accuracy across varying signal-to-noise ratios.
3Object-affected harmful factors
If noise suppression processing is applied to sensor signals, then unnecessary components are reduced, but the complexity of the processing system increases
Solution Approach 1:
The system performs self-service by using the input signal itself to generate the restoration signal through learned patterns. The predictive models are trained once and then autonomously perform noise suppression without requiring complex real-time processing, reducing system complexity while maintaining effectiveness.
Data Source
AI summary
According to an embodiment, a first signal processing apparatus includes one or more hardware processors configured to function as a signal processing unit, a first signal adjustment unit, and an update unit. The signal processing unit is configured to perform first signal processing on an input signal in which a second signal is superimposed on a first signal using at least one parameter and to output a first restoration signal obtained by restoring the first signal. The first signal adjustment unit is configured to multiply at least one of the first signal and the first restoration signal by a weighting coefficient and to output a corrected first signal and a corrected first restoration signal. The update unit is configured to update the at least one parameter using an error between the corrected first signal and the corrected first restoration signal.