Multichannel Audio Metadata Compression via KLT Eigenchannels
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Solution Overview
Problem
Conventional Karhunen-Loève Transform (KLT)-based multichannel audio coding methods require high metadata bitrates for maintaining audio quality, limiting their applicability to specific numbers of audio channels and being inefficient for arbitrary channel configurations, especially with advanced recording devices like the Eigenmike.
Innovation Solution
An apparatus and method utilizing a KLT-based pre-processor to transform input audio channels into eigenchannels, with a metadata re-arrangement unit that re-arranges metadata elements into multi-dimensional blocks for efficient encoding, employing machine learning to determine correlation values and optimize the re-arrangement scheme for increased compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional KLT-based audio coding is used to process multichannel audio signals, then audio quality can be maintained, but metadata bitrate becomes excessively high
Solution Approach 1:
The patent segments the metadata into multiple categories (spatial parameters, spectral parameters, temporal parameters, etc.) and applies different compression techniques to each category. This segmentation allows targeted optimization of each metadata type, reducing overall bitrate while preserving audio quality.
Solution Approach 2:
The patent introduces a new dimension of organization by arranging metadata elements in a two-dimensional array structure (metadata matrix) with rows representing different metadata types and columns representing different time frames. This dimensional transformation enables more efficient compression through block-based processing and exploitation of temporal correlations.
2Measurement precision
If conventional KLT-based audio coding is used, then audio quality is preserved, but the system is limited to specific numbers of audio channels
Solution Approach 1:
The patent creates a universal metadata structure that can accommodate any number of input channels and arbitrary microphone configurations. The KLT-based processing and metadata organization are designed to be channel-agnostic, allowing the same framework to handle 5.1, 7.1, Ambisonics, and custom microphone array configurations uniformly.
Solution Approach 2:
The patent employs dynamic adaptation of the metadata structure based on the actual input signal characteristics. The number of eigenchannels, spatial parameters, and metadata elements are dynamically determined by the input configuration rather than being fixed, enabling flexible adaptation to different channel counts and microphone arrangements.
3Quantity of substance
If Vector Quantizer compression is applied to metadata, then bitrate is reduced, but implementation complexity increases and codebook training becomes difficult
Solution Approach 1:
The patent extracts and removes redundant information from the metadata before compression. By identifying and eliminating correlated parameters, temporal redundancies, and insignificant details, the metadata size is reduced inherently, requiring less aggressive compression and avoiding the need for complex codebook-based techniques.
Solution Approach 2:
The patent uses simple, computationally efficient compression techniques (such as quantization and entropy coding) that are easy to implement and require minimal training data. These lightweight methods achieve adequate compression ratios without the implementation complexity and training requirements of Vector Quantizers.
4Device complexity
If small VQ codebook size is used for metadata compression, then implementation is simpler, but representation quality deteriorates
Solution Approach 1:
The patent performs preliminary processing of metadata including normalization, filtering, and redundancy removal before compression. This preliminary action reduces the effective information content that needs to be represented, allowing smaller codebooks or simpler compression schemes to achieve the same representation quality that would otherwise require large codebooks.
Data Source
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AI summary
The invention relates to an apparatus (110) for encoding an input audio signal, wherein the input audio signal comprises a plurality of input audio channels. The apparatus (110) comprises a KLT-based pre-processor (111) configured to transform the plurality of input audio channels into a plurality of eigenchannels and to provide metadata in the form of a plurality of metadata elements, wherein the metadata allows reconstructing the plurality of input audio channels on the basis of the plurality of eigenchannels, a metadata rearrangement unit (114) configured to re-arrange the plurality of metadata elements on the basis of a re-arrangement scheme into one or more metadata blocks, wherein each of the one or more metadata blocks is a multi-dimensional array, and a metadata encoder (115) configured to encode each of the one or more metadata blocks.