Spectral Peak Position Coding Across Periodic and Sparse Distributions
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Solution Overview
Problem
Conventional methods for encoding spectral peak positions in audio signals are inefficient due to abrupt changes in distribution, particularly when peaks are semi-periodic or clustered, leading to high bit consumption.
Innovation Solution
Implementing a closed-loop system that switches between delta coding and sparse coding schemes based on peak distribution characteristics, using delta coding for periodic distributions and sparse coding for sparse distributions, with Huffman coding for delta values and bit vector compression for sparse coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a single lossless coding scheme (e.g., Huffman coding) is used for spectral peak positions, then the coding is simple and reliable, but the bit consumption is high due to inability to adapt to different peak distributions
Solution Approach 1:
The patent applies dynamics by switching between different coding schemes (delta coding and sparse coding) based on the detected peak distribution characteristics. The system dynamically selects the appropriate coding mode: delta coding when peaks are semi-periodic, and sparse coding when peaks are clustered, thereby adapting to different signal conditions to minimize bit consumption while maintaining lossless compression
Solution Approach 2:
The patent changes the coding parameter (coding scheme selection) based on the statistical properties of the spectral peak positions. By analyzing the peak distribution and selecting different coding approaches (delta coding for semi-periodic patterns, sparse coding for clustered patterns), the system optimizes the bit representation according to the actual data characteristics
2Quantity of substance
If delta coding is used for spectral peak positions, then periodic distributions are encoded efficiently, but bit consumption increases when peaks are clustered or sparsely distributed
Solution Approach 1:
The patent applies local quality by using different coding strategies for different local characteristics of the peak distribution. Delta coding is applied locally when peaks exhibit semi-periodic behavior, while sparse coding is applied locally when peaks are clustered or sparsely distributed, ensuring optimal compression for each local pattern
3Quantity of substance
If sparse coding is used for spectral peak positions, then clustered distributions are encoded efficiently, but bit consumption increases when peaks are semi-periodic
Solution Approach 1:
The patent applies local quality by using different coding strategies for different local characteristics of the peak distribution. Delta coding is applied locally when peaks exhibit semi-periodic behavior, while sparse coding is applied locally when peaks are clustered or sparsely distributed, ensuring optimal compression for each local pattern
Data Source
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AI summary
A coder and decoder, and methods therein, are provided for coding and decoding of spectral peak positions in audio coding. According to a first aspect, an audio signal segment coding method is provided for coding of spectral peak positions. The method comprises determining which one out of two lossless spectral peak position coding schemes that requires the least number of bits to code the spectral peak positions of an audio signal segment; and selecting the spectral peak position coding scheme that requires the least number of bits to code the spectral peak positions of the audio signal segment. A first one of the two lossless spectral peak position coding schemes is suitable for periodic or semi-periodic spectral peak position distributions; and a second one of two lossless spectral peak position coding schemes is suitable for sparse spectral peak position distributions.