Neural Network Signal Processing Device Optimizing Loss for Noise Suppression
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Solution Overview
Problem
Conventional signal processing techniques face challenges in achieving high accuracy for latter-stage signal processing when noise intensity is significantly higher than signal intensity, as off-the-shelf neural networks tend to suppress signal components, leading to inadequate noise removal and reduced performance in industrial applications.
Innovation Solution
A signal processing device that performs early-stage signal processing using a neural network, optimizing parameters based on loss related to the accuracy of latter-stage signal processing, converting output signals to calculate loss, and using a combination of loss types to optimize neural network performance, thereby enhancing noise suppression without affecting signal components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional neural networks are used for signal processing, then the network can process signals, but the signal components are suppressed when noise intensity is significantly higher than signal intensity
Solution Approach 1:
The patent implements feedback by calculating the accuracy of latter-stage signal processing results and using this accuracy information to optimize the parameters of the early-stage neural network. The loss function incorporates the accuracy of subsequent processing stages, creating a feedback loop that guides parameter optimization to preserve signal components while removing noise.
Solution Approach 2:
The patent changes the parameter optimization approach by using a composite loss function that combines traditional loss terms with accuracy-based loss from latter-stage processing. This parameter change in the optimization objective function enables the network to learn parameters that preserve signal components while achieving effective noise removal.
2Object-affected harmful factors
If noise removal preprocessing is performed, then noise can be reduced, but the latter-stage signal processing cannot achieve high accuracy when noise intensity is significantly higher than signal intensity
Solution Approach 1:
The patent performs preliminary action by optimizing early-stage signal processing parameters before latter-stage processing occurs. The neural network parameters are pre-optimized using a loss function that anticipates the requirements of subsequent processing stages, ensuring that the processed signals are suitable for high-accuracy latter-stage analysis even in low signal-to-noise ratio conditions.
Solution Approach 2:
The patent uses feedback from latter-stage processing accuracy to guide early-stage parameter optimization. The accuracy metrics from subsequent processing stages are fed back into the loss function, creating a closed-loop system that ensures noise removal does not compromise the accuracy requirements of later processing steps.
3Speed
If early-stage signal processing is optimized independently, then the processing speed can be maintained, but the overall system accuracy is limited
Solution Approach 1:
The patent merges the optimization objectives of early-stage and latter-stage signal processing by combining their respective loss functions into a composite loss function. This unified optimization approach maintains processing efficiency while improving overall system accuracy, as the early-stage processing is optimized considering the requirements of subsequent stages without requiring reprocessing.
Data Source
AI summary
A signal processing apparatus includes one or more processors. The one or more processors perform first-type signal processing on an input signal using a neural network, and output a first-type output signal. The one or more processors convert the first-type output signal into a second-type output signal for calculating a first-type loss related to accuracy of second-type signal processing performed by another signal processing device The one or more processors calculate the first-type loss based on the second-type output signal and a correct signal. The one or more processors optimize parameters of the neural network based on the first-type loss.