Audio Encoding Using Apple-Peeling Codebook for Low Delay
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Solution Overview
Problem
Existing audio encoding methods face challenges in achieving low delay and complexity, particularly in linear predictive coding, where high algorithmic delay and computational complexity hinder efficient audio data transmission and compression.
Innovation Solution
A method and device for encoding audio data using linear prediction combined with vector quantization based on a gain-shape vector codebook, employing an apple-peeling method and a coding tree with a lookup table for efficient codebook construction and reduced complexity, allowing for low delay and efficient audio data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear predictive coding is used for audio encoding, then audio quality is improved, but algorithmic delay and computational complexity increase
Solution Approach 1:
The audio signal is divided into frames of 160 samples (8 ms at 20 kHz sampling rate), and further segmented into subbands using a filter bank. This segmentation allows parallel processing of different frequency components, reducing overall encoding delay while maintaining audio quality through localized analysis.
Solution Approach 2:
The patent pre-calculates and stores codebooks containing quantized excitation vectors and linear prediction coefficients during an offline training phase. These pre-prepared codebooks are directly used during real-time encoding without requiring complex online optimization, significantly reducing algorithmic delay while preserving audio quality.
2Measurement precision
If linear predictive coding is used for audio encoding, then audio quality is improved, but computational complexity increases
Solution Approach 1:
The encoding process is segmented into independent stages: filter bank analysis, linear prediction for each subband, vector quantization, and synthesis. Each stage processes only specific portions of the signal, allowing parallel implementation and reducing overall computational complexity while maintaining high audio quality through comprehensive processing.
Solution Approach 2:
The patent uses pre-trained codebooks that are copied and applied to each audio frame during encoding. Instead of performing complex optimization calculations for every frame, the system copies appropriate code vectors from the pre-trained codebooks, dramatically reducing computational complexity while maintaining audio quality through the use of optimized reference data.
3Measurement precision
If vector quantisation with large codebook is used, then encoding precision is improved, but search complexity and memory requirements increase
Solution Approach 1:
The large codebook is segmented into multiple subcodebooks organized by excitation type (periodic, aperiodic, mixed) and frequency range. The search process is divided into stages where different subcodebooks are searched sequentially based on signal characteristics, reducing overall search complexity while maintaining encoding precision through comprehensive coverage of different signal types.
Solution Approach 2:
The codebooks are pre-organized and sorted during offline training based on similarity metrics and signal characteristics. During real-time encoding, this pre-organization allows the system to quickly identify and search only relevant portions of the codebook, dramatically reducing search complexity while maintaining high encoding precision through targeted search strategies.
4Measurement precision
If more code vectors are stored in codebook, then quantisation error is reduced, but memory requirements and processing complexity increase
Solution Approach 1:
The codebook is segmented into multiple smaller subcodebooks organized by excitation type and frequency characteristics. Each subcodebook contains vectors optimized for specific signal conditions, allowing the system to use smaller, more manageable memory structures while collectively providing comprehensive coverage that reduces quantisation error across all audio types.
Solution Approach 2:
The codebook vectors are pre-selected and pre-organized during offline training to maximize coverage of typical speech and audio signals. This preliminary action ensures that the most frequently needed vectors are included and easily accessible, reducing the total number of vectors needed while maintaining low quantisation error through intelligent selection rather than brute-force inclusion of all possible vectors.
Data Source
AI summary
A wideband audio coding concept is presented that provides good audio quality at bit rates below 3 bits per sample with an algorithmic delay of less than 10 ms. The concept is based on the principle of Linear Predictive Coding (LPC) in an analysis-by-synthesis framework. A spherical codebook is used for quantisation at bit rates which are higher in comparison to low bit rate speech coding for improved performance for audio signals. For superior audio quality, noise shaping is employed to mask the coding noise. In order to reduce the computational complexity of the encoder, the analysis-by synthesis framework has been adapted for the spherical codebook to enable a very efficient excitation vector search procedure. Furthermore, auxiliary information gathered in advance is employed to reduce a computational encoding and decoding complexity at run time significantly. This auxiliary information can be considered as the SCELP codebook. Due to the consideration of the characteristics of the apple-peeling-code construction principle, this codebook can be stored very efficiently in a read-only-memory.