Audio Codec Magnitude Sorting for CPU Efficiency
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Solution Overview
Problem
Existing audio decoders face challenges in efficiently processing audio signals, particularly quiet audio, which can lead to intense CPU processing demands, resulting in resource inefficiency and potential degradation of listening experience.
Innovation Solution
The proposed solution involves arranging audio components by magnitude and dynamically establishing an elimination threshold, where audio components below the threshold are not processed, allowing for reduced CPU processing while maintaining audio quality by only processing components above the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all audio components are processed to maintain audio quality, then listening experience is preserved, but CPU processing demands become intense
Solution Approach 1:
The patent applies partial action by selectively processing only significant audio components while eliminating quiet components below a perceptible threshold. The decoder processes audio components based on their magnitude, applying full processing only when necessary (above threshold) and skipping processing when unnecessary (below threshold), thus reducing CPU workload while maintaining perceptual audio quality.
Solution Approach 2:
The patent implements local quality by differentiating processing intensity based on local characteristics of audio components. Each audio component is evaluated individually based on its magnitude, and processing is applied locally only to components that meet the significance threshold, rather than uniformly processing all components.
2Productivity
If quiet audio components are eliminated from processing, then CPU resources are conserved, but audio resolution is reduced
Solution Approach 1:
The patent changes the parameter of audio component significance by introducing a magnitude-based threshold. Audio components are transformed from a uniform processing model to a selective processing model based on their amplitude parameter, eliminating components below the threshold while preserving those above it, thus balancing resource efficiency with audio quality.
3Productivity
If audio components are arranged by magnitude and processed selectively, then processing efficiency is improved, but decoder complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-arranging audio components in magnitude order and pre-determining which components meet the significance threshold before full processing. This preliminary sorting and threshold evaluation is performed on the encoded audio data structure, enabling the decoder to skip unnecessary processing steps and improving overall processing efficiency despite the added organizational complexity.
Data Source
AI summary
Techniques are described for audio decoding for, in an example, computer games. Audio is delivered in packets. The components of a packet are sorted in the time domain or the frequency domain by magnitude. An elimination threshold can be dynamically established with components below the threshold being eliminated from processing by the receiver, to save processing requirements.