Adaptive Audio Decoder with Predictor-Based Noise Shaping
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Solution Overview
Problem
Existing audio signal encoding and decoding technologies, such as differential pulse code modulation (DPCM) and adaptive differential pulse code modulation (ADPCM), face challenges in efficiently managing dynamic range variations and noise reduction, particularly in maintaining a high signal-to-noise ratio across varying sampling frequencies.
Innovation Solution
The implementation of a decoder with an inverse quantizer and a predictor circuit, coupled with a low-pass filter having fixed filter coefficients, and an inverse adaptive noise shaping filter, which adjusts quantization step sizes based on a loading factor and leakage coefficient to control bit rate and noise shaping, ensuring efficient encoding and decoding of audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If adaptive differential pulse code modulation (ADPCM) is used to reduce bit rate, then the quantity of data transmitted is reduced, but the signal-to-noise ratio deteriorates due to varying dynamic range
Solution Approach 1:
The patent implements dynamic adjustment of quantization step sizes based on the instantaneous dynamic range of the audio signal. The quantizer adapts its step size in real-time to match the signal characteristics, allowing efficient bit rate reduction while preserving signal-to-noise ratio. This is achieved through feedback mechanisms that continuously monitor signal energy and adjust quantization parameters accordingly.
Solution Approach 2:
The patent changes the quantization parameter (step size) dynamically based on signal conditions. By varying the quantization step size according to the signal's dynamic range, the system optimizes the balance between bit rate reduction and signal-to-noise ratio maintenance, preventing the degradation that occurs with fixed-step ADPCM.
2Measurement precision
If quantization step size is reduced to improve signal-to-noise ratio, then the measurement precision is improved, but the device complexity increases due to adaptive control mechanisms
Solution Approach 1:
The patent divides the decoding process into distinct functional modules: inverse quantizer, predictor circuit, and low-pass filter. Each module handles a specific aspect of signal reconstruction, which simplifies the overall design by localizing complexity in manageable segments rather than requiring a monolithic complex structure.
Solution Approach 2:
The patent inverts the traditional approach by using fixed filter coefficients in the low-pass filter and compensating for adaptivity in the inverse quantizer and predictor stages. This inversion simplifies the filtering portion of the decoder while maintaining adaptive signal processing where it is most needed, thereby reducing overall device complexity.
3Productivity
If sampling frequency is reduced to decrease data rate, then the productivity is improved, but the signal quality deteriorates due to aliasing and reduced frequency response
Solution Approach 1:
The patent applies preliminary low-pass filtering at the encoder output to pre-condition the signal before transmission. This preliminary action prepares the signal for lower sampling rates by removing high-frequency components that would cause aliasing, thereby enabling reduced data rates without significant quality loss.
Solution Approach 2:
The patent employs feedback mechanisms where the decoder uses knowledge of the encoder's low-pass filter characteristics to adjust its reconstruction process. The predictor circuit and inverse quantizer use feedback from previous samples and signal statistics to compensate for the effects of reduced sampling frequency, maintaining signal quality despite lower productivity.
Data Source
AI summary
A decoder generates decoded signals based on quantized signals. The decoder includes an inverse quantizer and a predictor circuit. The quantized signals are generated in an encoder by low-pass filtering an input signal and encoding the filtered signal using adaptive differential pulse code modulation. The predictor circuit has filter coefficients based on a frequency response of the low-pass filter used to filter the input signal.