Audio Watermark Decoding With Time Shifts for Robust Detection
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Solution Overview
Problem
Existing audio watermark detection systems face challenges in accuracy due to interference and noise, leading to improper device responses when detecting voice commands or commands embedded in audio data.
Innovation Solution
The implementation of extended audio watermarks with time and frequency extensions, along with multiple time shifts in decoding, enhances detection accuracy by increasing the duration and redundancy of the watermark, allowing for improved compatibility and robust detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio watermark detection is performed using conventional methods, then the detection process is simple and fast, but the detection accuracy is low due to interference and noise
Solution Approach 1:
The audio watermark detection process is divided into multiple decoding attempts with different time shifts. Each decoding attempt processes a segment of the audio data with a specific time offset, allowing the system to systematically search for the correct watermark position without requiring a single complex decoding operation.
Solution Approach 2:
The system performs preliminary actions by trying multiple time shifts before final watermark detection. Each time shift attempt is a preliminary step that prepares the decoding process with different temporal offsets, ensuring that the correct watermark can be detected even when its position is uncertain.
2Reliability
If extended audio watermarks with time and frequency extensions are used, then detection accuracy and robustness are improved, but the processing time and computational load increase
Solution Approach 1:
The system employs periodic action by implementing multiple decoding attempts at regular intervals with different time shifts. This periodic approach systematically explores different temporal positions of the extended watermark, ensuring robust detection while maintaining a structured processing rhythm that optimizes computational efficiency.
Solution Approach 2:
The system changes parameters by varying the time shift values across multiple decoding attempts. Each attempt uses a different time shift parameter, allowing the system to adapt to the extended watermark's temporal characteristics and improve detection robustness without requiring a complete redesign of the decoding architecture.
3Measurement precision
If multiple time shifts are used in decoding, then false triggers are reduced and command interpretation accuracy is improved, but the number of processing operations increases
Solution Approach 1:
The system uses copying by creating multiple copies of the decoding process with different time shifts. Each copy represents a separate decoding attempt that independently processes the audio data with a specific temporal offset, allowing the system to identify the correct watermark position through comparison without requiring a single overly complex decoding operation.
Data Source
AI summary
Described herein is a system for performing watermark detection using multiple time shifts to increase a resolution of watermark detection. Instead of decoding blocks of successive audio frames (e.g., 10 ms of audio) using a single watermark decoder, watermark verification can be performed by decoding overlapping frame shifts using multiple decoders in parallel, thereby increasing a chance that one of the watermark decoders will be synchronized with the embedded audio watermark. For example, watermark verification may split audio data into parallel streams and decode using two decoders (e.g., 2× shifts-per-frame), four decoders (e.g., 4× shifts-per-frame), or the like. Increasing resolution by performing overlapping detection increases an accuracy of the watermark detection without changing the embedded audio watermark.