Merged Audio Code Layers With Error Correction for Media Identification
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Solution Overview
Problem
Conventional Critical Band Encoding Technology (CBET) for audience measurement is insufficient in handling large media collections, as it requires more efficient coding methods and error correction techniques to decode ancillary audio codes effectively, especially when dealing with millions or billions of codes.
Innovation Solution
The method involves encoding message symbols as substantially single-frequency components within two encoded layers of audio data, using synchronization between layers for detection, and employing error correction techniques like Reed-Solomon or convolutional code error correction to decode and correct errors in the detected symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional CBET encoding uses multiple separate parallel encoding layers, then the system can handle tens of thousands of codes, but it becomes insufficient for identifying and characterizing media collections numbering in the millions or billions
Solution Approach 1:
The patent merges multiple separate parallel encoding layers into a single unified encoding layer structure. Instead of maintaining separate layers for different code sets, the invention combines them into one integrated layer that can represent millions or billions of codes through a unified symbol mapping scheme, thereby reducing structural complexity while expanding code capacity.
Solution Approach 2:
The patent transitions from a one-dimensional approach (separate parallel layers) to a multi-dimensional approach within a single layer by using multiple symbol alphabets and hierarchical coding schemes. This allows the system to encode vastly more codes within the same structural framework by utilizing additional encoding dimensions such as symbol value ranges and layered symbol groups.
2Quantity of substance
If encoding layers are merged to enable more efficient coding for larger media collections, then code capacity increases to millions or billions, but error correction becomes more challenging to ensure proper encoding and decoding
Solution Approach 1:
The patent incorporates error correction codes and validation mechanisms directly into the encoding process before data is transmitted or stored. By pre-calculating and embedding correction capabilities in the encoded structure, the system maintains reliability even as code capacity expands to millions or billions of identifiers.
Solution Approach 2:
The patent implements feedback mechanisms where the decoded data is validated against expected patterns and error correction codes. If errors are detected during decoding, the system uses the embedded correction information to identify and correct deviations, ensuring accurate retrieval even from the expanded code space.
3Adaptability or versatility
If more codes are embedded within audio data to characterize larger media collections, then the identification capability improves, but the complexity of detecting and measuring the embedded codes increases
Solution Approach 1:
The patent segments the large code space into multiple smaller symbol alphabets or code groups, each manageable by standard detection algorithms. By dividing the overall encoding into segments that can be detected and processed independently, the system maintains detection simplicity while achieving the ability to identify vast media collections through combined segment interpretation.
Data Source
AI summary
Apparatus, system and method for encoding and decoding ancillary code for digital audio, where multiple encoding layers are merged. The merging allows a greater number of ancillary codes to be embedded into the encoding space, and further introduces efficiencies in the encoding process. Utilizing certain error correction techniques, the decoding of ancillary code may be improved and made more reliable.