Frequency-Masked Audio Watermarking for Noise-Robust Detection
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Solution Overview
Problem
Existing methods for audio watermarking and detection face challenges in robustness and accuracy, particularly in noisy environments, and there is a need for efficient techniques to embed and extract codes in audio signals without affecting the audio quality.
Innovation Solution
The method involves encoding audio signals with inaudible codes using frequency masking and error correction techniques, employing stacking processes to enhance detection, and validating messages to improve robustness against noise and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If audio signals are encoded with watermarks using frequency masking, then the watermark becomes imperceptible to listeners, but detection accuracy deteriorates in noisy environments
Solution Approach 1:
The audio signal is divided into multiple frequency bands using a filter bank, allowing the watermark to be embedded in specific frequency regions where it is least perceptible. This segmentation enables selective placement of watermark energy in imperceptible frequency regions while maintaining detectability through targeted analysis of those same regions.
Solution Approach 2:
The system performs preliminary analysis of the audio signal to identify imperceptible frequency regions before embedding the watermark. By pre-characterizing the audio signal's spectral content and masking properties, the system can optimally position watermark energy in regions that will not be perceived by listeners, thereby improving both imperceptibility and subsequent detection accuracy.
2Reliability
If stacking processes are employed to enhance watermark detection, then detection robustness improves, but processing time and computational complexity increase
Solution Approach 1:
The system applies stacking to only the most critical frequency bands or time segments rather than processing the entire audio signal uniformly. By focusing computational resources on portions of the signal most likely to contain detectable watermark energy, the system achieves robust detection while minimizing overall processing time and computational complexity.
Solution Approach 2:
The watermark detection employs periodic sampling and stacking at strategically selected time intervals rather than continuous processing. This periodic approach maintains detection robustness by capturing sufficient statistical information through repeated measurements while significantly reducing computational load compared to continuous analysis.
3Reliability
If error correction techniques are used to improve robustness against noise, then detection reliability improves, but the complexity of the encoding and decoding processes increases
Solution Approach 1:
The system introduces intermediate processing stages that simplify the overall encoding and decoding complexity. By breaking down the error correction process into modular intermediate steps with well-defined interfaces, the system achieves robust noise resistance while maintaining manageable complexity through structured, incremental processing rather than monolithic complex algorithms.
Data Source
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AI summary
Methods and apparatus to audio watermarking and watermark detection and extracted are described herein. An example method includes receiving a media content signal, sampling the media content signal to generate samples, storing the samples in a buffer, determining a first sequence of samples in the buffer, determining a second sequence of samples in the buffer, wherein the second sequence of samples is of substantially equal length as the first sequence of samples, calculating an average of the first sequence of samples and the second sequence of samples to generate an average sequence of samples, extracting an identifier from the average sequence of samples, and storing the identifier in a tangible memory.