Radio Receiver Spit Noise Detection Using Stored Noise Profiles
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Solution Overview
Problem
Existing radio systems face challenges in accurately estimating the noise floor, leading to undesirably aggressive attenuation or high noise levels due to difficulties in noise floor estimation, particularly with wideband adjacent channel noise known as 'spit' noise.
Innovation Solution
Implementing a radio receiver with a Fourier transform engine and spectral processing engine that generates noise floor estimates using a stored noise profile and frequency domain block magnitudes, applying spectral processing techniques for noise suppression and spectrum-wide attenuation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise floor estimation methods are used, then the system can operate with simple processing, but the noise floor estimation accuracy deteriorates leading to aggressive attenuation or high noise levels
Solution Approach 1:
The system performs preliminary actions by storing noise profiles for different components (LNA, mixer, demodulator) in advance, and uses these pre-stored profiles to quickly estimate the noise floor without complex real-time calculations. The noise profile storage occurs before actual signal processing, enabling accurate and efficient noise floor estimation.
2Measurement precision
If spectral processing techniques are applied for noise suppression, then noise floor estimation accuracy improves, but the complexity of the processing system increases
Solution Approach 1:
The system segments the noise estimation process into distinct component-specific profiles (LNA noise profile, mixer noise profile, demodulator noise profile). Each component's noise characteristics are stored separately and can be independently selected and combined, simplifying the overall processing while maintaining high accuracy.
Solution Approach 2:
The system changes parameters by selecting different noise profiles based on operating conditions (e.g., which components are active). The spectral processing engine adjusts the noise floor estimate by combining relevant component profiles according to the current signal path configuration, enabling accurate adaptation without complex real-time analysis.
3Object-affected harmful factors
If aggressive attenuation is applied to suppress noise, then noise levels decrease, but audio quality deteriorates due to loss of desired signal
Solution Approach 1:
The system applies local quality by performing frequency-selective noise suppression. The spectral processing engine identifies and attenuates only the specific frequency components that correspond to noise, while preserving the desired signal frequencies. This localized approach ensures noise reduction without degrading overall audio quality.
4Productivity
If simple noise estimation is used, then processing speed is fast, but the system cannot accurately detect and mitigate spit noise
Solution Approach 1:
The system prepares in advance by storing detailed noise profiles for different components and conditions. When spit noise occurs, the spectral processing engine can quickly compare the current spectrum against these pre-stored profiles to identify and mitigate the noise, maintaining high processing speed while achieving accurate detection.
Data Source
AI summary
A method of detecting noise in an audio signal of a radio receiver includes transforming a time-domain audio signal into frequency-domain blocks, and using a stored noise profile corresponding to a component of the radio receiver, generating in the radio receiver at least a first metric based on the frequency-domain blocks. In addition, the method includes, in the radio receiver, based on the first metric, detecting a presence of noise intruding into an active channel from another channel.