Neural Network Compensates Digital Filter Distortions
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Solution Overview
Problem
Noise reduction in moving vehicles is challenging due to variable acoustic noise from vehicle speed, road conditions, and weather, which degrades the quality of speech and music by masking soft sounds and reducing intelligibility and fidelity.
Innovation Solution
A method and system combining a digital filter with a neural network to suppress noise from input signals, where the neural network is trained to compensate for distortions introduced by the digital filter, allowing for effective noise reduction and improved signal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a digital filter is used to suppress noise spectrum, then noise reduction is improved, but distortions are introduced in the signal of interest
Solution Approach 1:
A neural network is introduced as an intermediary component between the digital filter and the final output. The neural network receives the filtered signal and processes it to compensate for the distortions introduced by the digital filter, thereby maintaining signal quality while retaining noise suppression benefits
Solution Approach 2:
The system dynamically adjusts processing parameters by using a neural network that can adapt to different signal conditions. The neural network learns to optimize the balance between noise suppression and signal fidelity by adjusting its internal parameters based on the input signal characteristics
2Measurement precision
If noise suppression is applied to improve signal clarity, then intelligibility is improved, but processing time increases
Solution Approach 1:
The digital filter performs preliminary noise suppression in the frequency domain before the neural network processes the signal. This preliminary action reduces the computational burden on subsequent processing stages and enables more efficient real-time processing
Data Source
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AI summary
The technology described in this document can be embodied in a method for processing an input signal that represents a signal of interest captured in the presence of noise to generate a de-noised estimate of the signal of interest. The method includes receiving an input signal representing a signal of interest captured in the presence of noise. The method also includes processing at least a portion of the input signal using a digital filter to generate a filtered signal, the digital filter configured to suppress at least a portion of spectrum of the noise. The method further includes processing the filtered signal using a first neural network to generate a de-noised estimate of the signal of interest, wherein the first neural network is trained to compensate for distortions introduced by the digital filter in the signal of interest.