Robust Spectral Encoding for Distorted Audio Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for encoding and decoding inaudible auxiliary data in audio signals are not robust enough to withstand distortions such as repeated sampling, time scale changes, and compression/decompression operations, leading to weakened signal recovery and synchronization issues, especially when audio signals are re-formatted and uploaded to the internet.
Innovation Solution
The proposed solution involves improving spectral encoding and decoding methods by using Fourier magnitude data for initial synchronization, pre-filtering, and least squares methods to detect and decode embedded data, along with informed frequency selection and error correction techniques to enhance robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If spectral encoding methods are used to embed inaudible auxiliary data in audio signals, then data embedding capability is improved, but robustness against distortions (repeated sampling, time scale changes, compression) deteriorates
Solution Approach 1:
The patent applies pre-filtering to the Fourier magnitude data before decoding operations. This preliminary action prepares the data by reducing noise and enhancing signal characteristics before the actual decoding process, thereby improving robustness against distortions that occur during broadcasting and internet transmission.
Solution Approach 2:
The patent replaces traditional time-domain correlation methods with frequency-domain correlation methods. By transforming the audio blocks to Fourier magnitude data and performing correlation in the frequency domain, the system achieves better resistance to time-scale changes and sampling rate variations that commonly occur during digital signal processing and compression.
2Ease of operation
If traditional decoding methods are used, then synchronization is simplified, but accuracy of detecting embedded data under distortion deteriorates
Solution Approach 1:
The patent substitutes time-domain signal processing with frequency-domain signal processing. By converting audio blocks to Fourier magnitude data and performing all correlation and detection operations in the frequency domain, the system maintains synchronization capability while achieving superior accuracy in detecting embedded data even when the audio signal has undergone time-scale changes, resampling, or compression.
3Adaptability or versatility
If audio signals undergo repeated sampling and compression operations for internet distribution, then adaptability to different formats is improved, but signal quality and code strength deteriorate
Solution Approach 1:
The patent employs frequency-domain correlation instead of time-domain correlation to detect embedded codes. This substitution makes the detection process invariant to time-scale changes and sampling rate variations, allowing the system to recover codes from audio signals that have been repeatedly sampled, compressed, and transcoded for internet distribution without significant loss of code strength.
Solution Approach 2:
The patent applies pre-filtering to Fourier magnitude data before correlation operations. This preliminary processing step enhances the signal-to-noise ratio by suppressing noise components and preserving code-related frequency components, thereby strengthening the effective code signal strength even after multiple compression and sampling operations have degraded the original signal.
Data Source
AI summary
Spectral encoding methods are more robust when used with improved weak signal detection and synchronizations methods. Further robustness gains are achieved by using informed embedding, error correction and embedding protocols that enable signal to noise enhancements by folding and pre-filtering the received signal.