Adaptive Audio Codec Using Fixed Low-Pass Filter Coefficients
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Solution Overview
Problem
Existing audio signal encoding and decoding technologies face challenges in efficiently managing dynamic range variations and achieving optimal signal-to-noise ratios, particularly in adaptive differential pulse code modulation (ADPCM) systems, which affect the quality and efficiency of audio transmission.
Innovation Solution
The implementation of an ADPCM system with a low-pass filter and adaptive noise shaping, using determined filter coefficients and control parameters based on the frequency response, along with an adaptive quantizer and predictor circuit, to generate and control quantized signals and feedback signals, thereby optimizing bit rate and signal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If adaptive quantization is used to handle varying dynamic range, then encoding efficiency is improved, but system complexity increases due to the need for adaptive control mechanisms
Solution Approach 1:
The patent implements adaptive quantization by dynamically adjusting the quantization step size based on the dynamic range of the audio signal. The system transitions from fixed quantization to variable quantization where the step size adapts to signal conditions, improving encoding efficiency for signals with varying amplitude characteristics while managing complexity through controlled adaptation mechanisms.
Solution Approach 2:
The patent changes the quantization parameter (step size) based on signal characteristics. By modifying the quantization step size dynamically according to the input signal's dynamic range, the system achieves better encoding efficiency for different signal conditions without requiring complete redesign of the quantization architecture.
2Measurement precision
If higher sampling rates are used to improve audio fidelity, then signal quality is enhanced, but transmission bandwidth and processing requirements increase
Solution Approach 1:
The patent applies different quantization strategies based on sampling rate. For higher sampling rates, the system adjusts quantization parameters to optimize the balance between fidelity and bandwidth usage, allowing high-quality audio transmission while managing the increased data volume through efficient quantization at the appropriate bit depth.
3Reliability
If feedback loops are implemented to improve prediction accuracy, then signal-to-noise ratio is enhanced, but latency increases due to additional processing steps
Solution Approach 1:
The patent implements a feedback loop where the decoded signal is fed back to the predictor circuit to generate prediction signals. This feedback mechanism improves prediction accuracy and enhances the signal-to-noise ratio by continuously adapting the prediction based on actual decoded values, though it introduces additional processing delay that must be managed in real-time applications.
Data Source
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AI summary
An encoder includes a low-pass filter to filter input audio signals. The low-pass filter has fixed filter coefficients. The encoder generates quantized signals based on a difference signal. The encoder includes an adaptive quantizer and a decoder to generate feedback signals. The decoder has an inverse quantizer and a predictor. The predictor has fixed control parameters which are based on a frequency response of the low-pass filter. The predictor may include a finite impulse response filter having fixed filter coefficients. The decoder may include an adaptive noise shaping filter coupled between the low-pass filter and the encoder. The adaptive noise shaping filter flattens signals within a frequency spectrum corresponding to a frequency spectrum of the low-pass filter.