Band-Selective Voice Signal Quantization for Coding Efficiency
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Solution Overview
Problem
Current voice encoding and decoding methods do not efficiently manage quantization across different frequency bands of a voice signal, leading to suboptimal voice encoding and decoding efficiency.
Innovation Solution
A method and device for selectively quantizing and dequantizing voice signals by frequency bands, using analysis-by-synthesis (AbS) and inverse Fourier transforms (IDFT and IFF) to apply different codebooks to predetermined low-frequency and selected high-frequency bands, enhancing coding efficiency by focusing on bands with significant energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform quantization is applied to all frequency bands, then implementation is simple, but coding efficiency is suboptimal due to unnecessary transmission of low-energy band information
Solution Approach 1:
The frequency spectrum is divided into multiple bands (e.g., 8 bands from 0-8kHz), allowing selective quantization of individual bands based on their energy characteristics. This segmentation enables the system to apply different quantization strategies to different frequency regions, improving overall coding efficiency by excluding low-energy bands from transmission.
Solution Approach 2:
Different quantization treatments are applied to different frequency bands based on their local energy characteristics. High-energy bands receive full quantization while low-energy bands are excluded or given reduced quantization. This local differentiation optimizes the balance between transmission quality and efficiency for each specific frequency region.
2Measurement precision
If all frequency bands are quantized, then signal reconstruction is more accurate, but transmission bandwidth and processing load increase
Solution Approach 1:
Low-energy frequency bands are extracted and excluded from the quantization and transmission process. By identifying and removing these insignificant bands, the system reduces transmission data volume while maintaining signal reconstruction accuracy for the important high-energy bands that carry the essential voice information.
Solution Approach 2:
Instead of quantizing all frequency bands equally, the system applies quantization only to the necessary high-energy bands that contribute significantly to voice quality. This partial action approach achieves adequate signal reconstruction accuracy with reduced transmission requirements by focusing resources on the most important frequency regions.
3Productivity
If band-selective quantization is implemented, then coding efficiency improves, but the complexity of selecting and processing specific bands increases
Solution Approach 1:
The system performs preliminary analysis of frequency band energy distribution before the quantization stage. By pre-calculating the energy characteristics of each band and determining which bands to include or exclude, the system prepares the selection criteria in advance, simplifying the subsequent quantization and transmission processes while maintaining high encoding efficiency.
Solution Approach 2:
The quantization system uses its own energy analysis capabilities to automatically determine which frequency bands require quantization. The system self-regulates the selection of bands to be processed based on their inherent energy characteristics, eliminating the need for external control mechanisms and reducing overall system complexity.
Data Source
AI summary
The present invention relates to a method and device for quantizing voice signals in a band-selective manner. A voice decoding method may include inversely quantizing voice parameter information produced from a selectively quantized voice band and performing inverse transform on the basis of the inversely quantized voice parameter information. Thus, according to the present invention, coding/decoding efficiency in voice coding/decoding may be increased by selectively coding/decoding important information.