Audio Encoding Correction Coefficient for Auditory Sub-band Noise
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Solution Overview
Problem
Conventional audio signal encoding methods fail to accurately reflect the importance of noise levels in sub-bands important to auditory sense, leading to suboptimal audio quality and increased operational complexity due to high-resolution frequency analysis requirements.
Innovation Solution
An audio encoding device that calculates a correction coefficient based on the importance of each frequency component, correcting noise levels to generate accurate additional signal information, thereby reflecting the noise level importance of sub-bands in the auditory sense, while reducing operational complexity through normal-resolution frequency analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution frequency analysis is used to calculate noise levels in each sub-band, then the accuracy of noise level reflection in auditory-important sub-bands is improved, but the operational complexity increases
Solution Approach 1:
The patent introduces a correction coefficient as an intermediary element that mediates between the simple average noise level calculation and the desired auditory-important sub-band noise level reflection. The correction coefficient, calculated based on auditory importance weights and signal energy distribution, adjusts the noise levels of auditory-important sub-bands without requiring complex high-resolution frequency analysis, thus achieving accurate noise level reflection with reduced operational complexity
Solution Approach 2:
The patent changes the parameter of noise level calculation by introducing a correction coefficient that modifies the original noise level values. Instead of directly calculating noise levels through complex high-resolution analysis, the system calculates a correction coefficient based on auditory importance weights and signal energy, then applies it to adjust the noise levels of auditory-important sub-bands, achieving accurate representation with simpler operations
2Productivity
If the bit rate is lowered to reduce information amount, then the encoding efficiency is improved, but the audio quality deteriorates due to band restriction
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different sub-bands based on their auditory importance. Instead of uniformly restricting all frequency bands, the system identifies auditory-important sub-bands and applies correction coefficients specifically to their noise levels, ensuring that quality is maintained in perceptually critical regions while allowing greater compression in less important regions, thus achieving low bit-rate encoding with preserved audio quality
Solution Approach 2:
The patent changes the parameter distribution in the frequency domain by applying correction coefficients to noise levels of auditory-important sub-bands. This parameter adjustment ensures that perceptually critical frequency regions maintain adequate signal quality even at low bit rates, while other regions can be more aggressively compressed, achieving encoding efficiency without significant quality deterioration
3Ease of operation
If noise levels of all sub-bands are averaged uniformly, then the calculation simplicity is improved, but the reflection of auditory importance in sub-bands is lost
Solution Approach 1:
The patent applies local quality by treating different sub-bands differently based on their auditory importance. Instead of uniformly averaging all sub-band noise levels, the system calculates auditory importance weights for each sub-band and applies correction coefficients selectively to auditory-important sub-bands, thus maintaining calculation simplicity while preserving the reflection of auditory importance in the noise level representation
Data Source
AI summary
By using a high-range sub-band signal, a correction coefficient corresponding to importance of auditory sense is calculated to correct a noise level and generate additional signal information, thereby accurately reflecting the noise level of the sub-band important in the auditory sense. Thus, it is possible to calculate additional signal information reflecting the noise level of the sub-band important in the auditory sense according to importance with a small calculation amount. The calculation amount can further be reduced by using a correction coefficient based on the characteristic of an ordinary audio signal.