ADPCM Compressor Dynamic Gain Envelope Estimation
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Solution Overview
Problem
Existing audio communication technologies face challenges in achieving low latency and robustness against transmission errors in limited bandwidth environments without the use of a sideband channel, which is often infeasible or complex to implement.
Innovation Solution
The implementation of an adaptive differential pulse code modulation (ADPCM) technique that includes a difference element, scaling element, quantizer, multiplier, predictor, and envelope estimator with dynamic gain adjustment, allowing for robust error correction and low latency audio communication without the need for a sideband channel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robustness against transmission errors is improved using conventional ADPCM techniques, then error resilience is enhanced, but reproduction quality and compression rate deteriorate significantly
Solution Approach 1:
The patent applies dynamics by making the quantization step size adaptive rather than fixed. The quantization step is dynamically adjusted based on the local signal characteristics (envelope estimation) to optimize both error resilience and reproduction quality. This dynamic adaptation allows the system to maintain high quality in good conditions while becoming more robust in error-prone conditions without permanently sacrificing quality.
Solution Approach 2:
The patent changes the parameter of quantization step size based on the estimated signal envelope. By varying this critical parameter according to signal characteristics, the system achieves optimal balance between compression efficiency, reproduction quality, and error robustness across different signal conditions without requiring a sideband channel.
2Productivity
If low latency audio communication is achieved in limited bandwidth environments, then bandwidth efficiency is improved, but robustness against transmission errors deteriorates
Solution Approach 1:
The adaptive quantization mechanism dynamically adjusts the quantization step size based on signal envelope estimation, allowing the system to maintain robust error correction capabilities while operating efficiently within limited bandwidth. This dynamic adaptation enables the system to optimize its performance characteristics in real-time without requiring additional sideband communication channels.
3Reliability
If a sideband channel is used to enhance robustness against transmission errors, then error resilience is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential information needed for robust error correction from the main audio signal itself through envelope estimation. By deriving the quantization step adaptation information directly from the audio signal's local characteristics rather than requiring a separate sideband channel, the system achieves error resilience while avoiding the complexity of additional communication channels.
Solution Approach 2:
The system uses the audio signal itself to provide the information needed for robust transmission. The envelope estimation is derived from the signal's own characteristics, allowing the system to self-adjust its quantization parameters for optimal error resilience without external assistance or additional communication channels.
Data Source
AI summary
Audio streaming devices, systems, and methods may employ adaptive differential pulse code modulation (ADPCM) techniques providing for optimum performance even while ensuring robustness against transmission errors. One illustrative device includes: a difference element that produces a sequence of prediction error values by subtracting predicted values from audio samples; a scaling element that produces scaled error values by dividing each prediction error by a corresponding envelope estimate; a quantizer that operates on the scaled error values to produce quantized error values; a multiplier that uses the corresponding envelope estimates to produce reconstructed error values; a predictor that produces the next audio sample values based on the reconstructed error values; and an envelope estimator. The envelope estimator includes: an updater that applies a dynamic gain to the reconstructed error values to produce update values; and an integrator that combines each of the update values with the corresponding envelope estimate to produce a subsequent envelope estimate.