Multi-Channel Audio Encoding Using Semantic Similarity
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Solution Overview
Problem
Existing multi-channel audio encoding methods struggle to efficiently separate down-mixed channels, leading to deteriorated spatiality during decoding due to the lack of consideration for channel similarity.
Innovation Solution
The method involves determining the degree of similarity between channels using semantic information, extracting spatial parameters from similar channels, and down-mixing these channels to maintain spatiality, while encoding and decoding processes utilize these parameters to efficiently compress and restore multi-channel audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If multi-channel audio signals are down-mixed without considering channel similarity, then encoding complexity is reduced, but channel separation difficulty increases and spatiality deteriorates
Solution Approach 1:
The patent applies preliminary action by determining channel similarity and identifying similar channels before the down-mixing process. The encoding apparatus calculates similarity metrics between channels and pre-selects which channels should be down-mixed together, ensuring that the down-mixing operation maintains spatial characteristics while reducing encoding complexity.
Solution Approach 2:
The patent changes parameters by introducing semantic information-based similarity metrics to characterize channel relationships. By using parameters such as spectral similarity, temporal correlation, and semantic content matching, the system dynamically determines which channels are similar and should be down-mixed, thereby maintaining spatiality while simplifying the encoding process.
2Productivity
If semantic information processing is added to determine channel similarity, then channel separation efficiency improves, but encoding complexity increases
Solution Approach 1:
The patent extracts only the essential semantic information needed for channel similarity determination, rather than processing all audio signal characteristics. By selectively extracting relevant semantic features (such as dominant frequency ranges, temporal patterns, and content semantics), the system improves channel separation efficiency while limiting the increase in encoding complexity to only the necessary processing steps.
3Measurement precision
If spatial parameters are transmitted for all channels, then decoding accuracy is improved, but transmission bandwidth increases
Solution Approach 1:
The patent applies local quality by transmitting spatial parameters selectively rather than uniformly for all channels. Based on the determined channel similarity, the system transmits detailed spatial parameters only for channels that require them for accurate reconstruction, while using simplified or omitted parameters for similar channels that can be reconstructed from their counterparts, thereby reducing transmission bandwidth while maintaining decoding accuracy where needed.
Data Source
AI summary
A multi-channel audio signal encoding and decoding method and apparatus are provided. The multi-channel audio signal encoding method, the method including: obtaining semantic information for each channel; determining a degree of similarity between multi-channels based on the obtained semantic information for each channel; determining similar channels among the multi-channels based on the determined degree of similarity between the multi-channels; and determining spatial parameters between the similar channels and down-mixing audio signals of the similar channels.