RMS Adaptive Filtering for Low-Rate Ambient Noise Compensation
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Solution Overview
Problem
Existing Ambient Noise Compensation (ANC) systems face computational intensity and information loss when estimating ambient noise levels, leading to false noise detection and feedback loops, as they operate on high-rate audio signals and cannot integrate down sampling without losing relevant information.
Innovation Solution
The proposed ANC system processes down sampled mean square (MS) or root mean square (RMS) audio levels instead of the audio itself, using adaptive filters to estimate ambient noise levels, which reduces computational complexity and maintains privacy by transmitting only MS levels, allowing for lower sampling rates and efficient computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive filters operate on high-rate audio signals (8 kHz to 48 kHz), then accurate noise level estimation is achieved, but computational complexity and energy consumption increase significantly
Solution Approach 1:
The patent extracts only the essential feature (RMS level) from the full audio signal for noise estimation, rather than processing the complete high-rate audio signal. This extraction approach maintains measurement precision while dramatically reducing computational energy consumption by operating on down-sampled RMS values instead of full audio waveforms
Solution Approach 2:
The patent changes the processing parameter from full audio signals to down-sampled RMS level signals. By transforming the audio signal into its RMS representation and operating at lower sampling rates, the system achieves comparable noise estimation accuracy with significantly reduced computational complexity and energy usage
2Productivity
If down sampling is applied to reduce computational load, then processing efficiency improves, but relevant information in the audio signal is lost
Solution Approach 1:
The patent extracts the RMS level information from the audio signal before down-sampling, ensuring that the essential noise level information is preserved. By computing RMS values first and then down-sampling these extracted features rather than the full audio signal, the system avoids information loss while achieving computational efficiency
Solution Approach 2:
The patent performs preliminary RMS computation on the audio signal before down-sampling. This preliminary action ensures that the critical noise level information is captured and preserved in the RMS values, which can then be efficiently processed at lower sampling rates without losing relevant information
3Measurement precision
If full audio signals are transmitted for processing, then accurate noise estimation is achieved, but audio privacy and security are compromised
Solution Approach 1:
The patent extracts only the RMS level information from the audio signal for transmission and processing, rather than transmitting the full audio signal. This extraction approach maintains noise estimation accuracy while protecting audio privacy, as the transmitted RMS values contain no intelligible audio content that could be monitored or inferred
Solution Approach 2:
The patent creates a simplified representation (RMS level copy) of the audio signal that retains the essential noise level information needed for estimation. This copy can be transmitted and processed without compromising privacy, as it contains only aggregated energy information rather than the actual audio content
Data Source
AI summary
An apparatus instantiating a method for computing automatic noise compensation gain with an adaptive filter that receives ambient sound and source audio, down samples ambient sound and source audio, and filters the down sampled ambient sound and source audio to compute an RMS noise estimate from which a gain is determined. The present invention features highly efficient computations, as sampling rates are significantly lower. The present invention keeps audio data secure by transmitting only the mean-square audio levels instead of the audio itself.