Audio Watermarking via Correlation Modification
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Solution Overview
Problem
Existing audio watermarking methods face challenges in robustly embedding and extracting information from audio signals, particularly under conditions of noise and distortion introduced by compression techniques like MP3, AAC, and AC3, while minimizing perceptible distortion and maintaining high data integrity.
Innovation Solution
The proposed audio watermarking system employs a modulator/encoder that uses a filter bank to divide the audio signal into frequency bands, adds delayed and amplitude-modulated echoes to the original signal, and incorporates error correction and psychoacoustic modeling to control distortion, along with a demodulator/decoder that processes soft bits and weights to extract information, ensuring reliable data recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If the marking level is kept low to minimize perceptible distortion, then the audio quality is preserved, but the cross correlation between the original signal and the modulating sequence is overwhelmed by noise and distortion, reducing the ability to extract embedded data
Solution Approach 1:
The audio signal is divided into multiple frequency bands using a filter bank, and watermarking is applied independently to each band. This segmentation allows the system to optimize the marking level for each frequency band separately, maintaining low overall distortion while ensuring sufficient correlation strength in each band for reliable data extraction.
Solution Approach 2:
Different marking levels and modulation strategies are applied to different frequency bands based on their specific characteristics and human perception thresholds. This local optimization ensures that each band contributes maximally to data embedding while maintaining acceptable audio quality, resolving the contradiction between low distortion and reliable extraction.
2Reliability
If the modulating sequence is made very long to maintain data extraction capability at low marking levels, then the bit rate becomes very low, but the ability to extract embedded data is maintained
Solution Approach 1:
By segmenting the audio signal into multiple frequency bands, the system can use shorter modulating sequences in each band while collectively achieving the same or better data extraction reliability. This segmentation approach increases the effective bit rate without sacrificing extraction capability.
Solution Approach 2:
The system transitions from using a single long modulating sequence in one dimension to using multiple shorter sequences across multiple frequency bands (adding a frequency dimension). This dimensional change allows the system to achieve the same correlation strength through parallel processing, thereby increasing bit rate while maintaining extraction reliability.
3Reliability
If larger echoes are used to overcome limitations of low bit rate compression systems, then data extraction robustness is improved, but perceptible distortion of the audio increases
Solution Approach 1:
The watermarking is applied across multiple frequency bands with appropriately scaled echo magnitudes for each band. This segmentation allows the system to achieve robust data extraction through cumulative effect across bands while keeping individual echo magnitudes below perceptible thresholds, thus maintaining audio quality.
Data Source
AI summary
To convey information using an audio channel, an audio signal is modulated to produce a modulated signal by embedding additional information into the audio signal. Modulating the audio signal processing the audio signal to produce a set of filter responses; creating a delayed version of the filter responses; modifying the delayed version of the filter responses based on the additional information to produce an echo audio signal; and combining the audio signal and the echo audio signal to produce the modulated signal. Modulating the audio signal may involve employing a modulation strength, and a psychoacoustic model may be used to modify the modulation strength based on a comparison of a distortion of the modified audio signal relative to the audio signal and a target distortion.