Scalable Audio Signal Encoding Using Iterative Dirac Pulse Error Minimization
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Solution Overview
Problem
Existing audio signal coding methods lack scalability, which limits compatibility with conventional decoding methods and flexibility in adapting data rates and frame sizes, especially in limited data transmission channels, necessitating an improvement in signal quality.
Innovation Solution
A method based on Subband Adaptive Differential Pulse Code Modulation (SB-ADPCM) that iteratively compares digital error signals with predicted signals using Dirac pulses to determine reference signals with minimal error, allowing for scalable quality enhancement by transmitting information about these signals, and includes signal generators and control units to generate and manage these reference signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional coding methods (G.729, G.722) are used, then data transmission is efficient, but signal quality and adaptability to different frequency ranges are limited
Solution Approach 1:
The patent segments the audio signal into multiple subbands (low-band and high-band) and applies different coding strategies to each. The low-band uses conventional ADPCM coding for compatibility, while the high-band uses predictive coding for enhanced quality. This segmentation allows the system to achieve high signal quality through selective filtering and subband processing while maintaining compatibility with conventional decoders that only process the low-band portion.
Solution Approach 2:
The patent introduces a new dimension to the coding scheme by adding a high-band extension to the conventional low-band coding. This dimensional expansion allows the system to transmit additional quality information (high-band signal) without disrupting existing conventional decoding processes, thereby improving signal quality while preserving compatibility.
2Measurement precision
If data rate is increased to improve quality, then signal quality improves, but adaptability to limited transmission channels deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by allowing the high-band extension to be optionally transmitted and decoded. When transmission channel capacity is sufficient, the full high-band signal is transmitted to achieve high quality. When channel capacity is limited, the system can revert to conventional low-band only coding, providing dynamic adaptation to varying transmission conditions without sacrificing quality when resources are available.
Solution Approach 2:
The patent changes the coding parameters dynamically based on available bandwidth. The system can switch between different coding modes (conventional ADPCM vs. enhanced predictive coding with high-band extension) and adjust the amount of high-band information transmitted, thereby adapting to limited transmission channel capacity while maintaining optimal signal quality when possible.
3Measurement precision
If conventional ADPCM coding is used, then implementation is simple, but signal quality in extended frequency ranges is insufficient
Solution Approach 1:
The patent divides the frequency spectrum into segments (low-band and high-band) and applies appropriate coding complexity to each. The low-band uses simple conventional ADPCM coding, while the high-band uses more complex predictive coding only where needed for quality enhancement. This segmented approach improves signal quality in extended frequency ranges while keeping the overall system complexity manageable by maintaining simple coding in the majority of the frequency range.
Solution Approach 2:
The patent applies complex predictive coding only partially - specifically only to the high-band extension portion of the signal, rather than to the entire audio spectrum. This partial application of complex coding provides quality improvement where it matters most (in the extended frequency range) while avoiding the excessive complexity that would result from applying complex coding to the entire signal, thereby improving quality without proportionally increasing overall system complexity.
Data Source
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AI summary
The invention relates to a method for the scalable improvement of the quality of an encoding method according to IT-U Recommendation G.722, including the following steps: - a digital error signal (E) derived from an input signal to be encoded and a prognosis signal is compared in sections to a number of M*LN different reference signals in an iterative process having a number of repeated steps depending on the scope of the expansion, and the reference signal having a minimum error signal of a prescribed error criteria is derived therefrom, - the reference signals are each made up of equidistant Dirac impulses δ(n) according to (I), wherein off = [0.. M-1], indicates the distance of the first impulse from a zero time point, αP ∈ { α0,α1,..,αL-1 } indicates the amplitude value, M the distance between the individual pulses, N the number of pulses, and L the number of different levels, - the information about the reference signal having the minimum error signal is transmitted.