Audio Block Realignment for Channel Change Detection
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Solution Overview
Problem
Existing audio watermarking techniques struggle to accurately extract audio codes or signatures from audio blocks when channel changes occur during the audio block duration, leading to inaccurate or missing representations of the audio content.
Innovation Solution
The implementation of a channel change detection system that uses a neural network to identify channel changes and realign audio blocks to coincide with these changes, allowing for the extraction of audio codes and signatures without interference from multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio blocks are used for watermarking extraction, then audio content can be identified, but channel changes during the audio block cause inaccurate or missing representations
Solution Approach 1:
The audio stream is segmented into multiple shorter audio blocks instead of using single long audio blocks. This segmentation allows the system to capture channel changes that occur during the audio stream, ensuring that each audio block corresponds to a specific channel. The neural network processes these segmented blocks sequentially to identify channel changes and extract accurate audio codes for each channel segment.
Solution Approach 2:
The system dynamically adjusts the audio block processing based on detected channel changes. The neural network continuously monitors the audio stream for channel transitions and modifies the audio block boundaries accordingly. This dynamic adaptation ensures that audio codes are extracted from the correct channel segments, maintaining accuracy even when channels change frequently.
2Measurement precision
If channel change detection is implemented, then audio code extraction accuracy improves, but system complexity increases
Solution Approach 1:
A neural network is introduced as an intermediary component between the audio stream input and the audio code extraction process. The neural network performs channel change detection by analyzing audio features and identifying transitions between channels. This intermediary layer enables accurate channel-aware extraction without requiring complex manual processing or multiple separate systems.
Solution Approach 2:
The neural network performs multiple functions within a single unified architecture: it detects channel changes, determines audio block boundaries, and guides the extraction process. This multi-functional approach consolidates what could be separate complex systems into one integrated solution, managing overall system complexity while maintaining high identification accuracy.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed that improve short content identification in an audio stream through channel change detection and audio block realignment. Example instructions cause one or more processors to form a first audio block from an audio stream, detect whether the first audio block contains a channel change, the first audio block being one of a plurality of audio blocks the accessed audio, determine an offset of time from a beginning of the first audio block to when the channel change occurs in response to the channel change being detected in the first audio block, and create a new audio block aligned to start at the offset of time beyond the beginning of the first audio block using audio information from the audio stream, the new audio block to include a single channel of audio stream data.