Multiband Noise Gate Using Frequency-Bin Attenuation Scaling
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Solution Overview
Problem
Existing audio signal processing technologies face challenges in effectively filtering noise floors across multiple frequencies in audio signals captured by image capture devices, making it difficult to remove unwanted noise without affecting desired audio signals.
Innovation Solution
A multiband noise gate is implemented in image capture devices, which transforms audio signals into the frequency domain, separates them into frequency bands, and uses lookup tables to determine bin-specific and overall attenuation multipliers to scale and reduce noise, preserving low-level noises while minimizing noise floors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional filters are used to remove noise, then noise reduction is achieved, but desired audio signals are also attenuated along with the noise floor
Solution Approach 1:
The audio signal is divided into multiple frequency bands, allowing different attenuation levels to be applied to each band. This segmentation enables selective noise reduction in specific frequency ranges while preserving desired signals in other bands, resolving the contradiction between noise removal and signal preservation.
Solution Approach 2:
Different attenuation characteristics are applied to different frequency bands based on their specific noise profiles. Each band receives customized attenuation treatment rather than uniform filtering, allowing optimal noise reduction in each frequency range while maintaining audio quality where signals are present.
2Object-affected harmful factors
If aggressive noise filtering is applied, then noise floor is reduced, but low-level desired noises are also removed
Solution Approach 1:
The attenuation levels in each frequency band are dynamically adjusted based on the detected noise characteristics and signal presence. The system adapts attenuation multipliers in real-time, applying stronger attenuation where only noise is present and reducing or eliminating attenuation where desired signals are detected, thus preserving low-level desired noises while reducing noise floors.
Solution Approach 2:
The system continuously monitors each frequency band to detect the presence and level of desired signals, using this feedback to adjust attenuation levels accordingly. When low-level desired noises are detected, the feedback mechanism reduces attenuation in that band, preventing their removal while still maintaining noise floor reduction in bands where only noise is present.
3Object-affected harmful factors
If frequency domain processing is implemented, then selective noise attenuation is achieved, but computational complexity increases
Solution Approach 1:
The frequency domain is divided into a limited number of discrete bands, reducing the computational complexity compared to processing every individual frequency point. This segmentation allows selective attenuation to be applied to manageable frequency groups, achieving noise reduction capability while keeping processing requirements feasible for implementation.
Data Source
AI summary
The present disclosure relates to processing a plurality of audio signals. The method includes receiving the plurality of audio signals in the frequency domain and determining an overall attenuation multiplier based on the plurality of audio signals and an overall lookup table that relates decibel values to different overall attenuation multipliers. The method further includes determining an attenuation vector comprising a plurality of bin-specific attenuation multipliers, each bin-specific attenuation multiplier respectively corresponding to a different frequency bin of the plurality of frequency bins. The method further includes scaling each bin-specific attenuation value in the attenuation vector with the overall attenuation multiplier, and editing each of the audio signals based on the scaled bin-specific attenuation values in the attenuation vector.