Audio Data Hiding with Codec-Independent Waveform Recovery
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Solution Overview
Problem
Existing data hiding techniques in audio steganography fail to survive linear prediction-based speech coding protocols, rendering embedded data corrupted during compression and decompression, and lack independence from speech codec specifications.
Innovation Solution
A method using voice samples as a medium for embedding and recovering data, independent of speech codec specifications, by superimposing a hidden data sequence on cover audio at a fraction of its amplitude, and recovering it through matched filtering and interpolation, treating speech compression as a 'black box'.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear prediction-based speech coding protocols are used to compress audio, then audio transmission efficiency is improved, but embedded data is corrupted during compression and decompression
Solution Approach 1:
The patent introduces an intermediary extraction process that isolates embedded data from the compressed audio stream before final decompression. By using correlation-based detection on the compressed bits and selectively extracting potential data segments, the system mediates between the compression process and data recovery, preventing corruption from propagating through the full decompression pipeline.
Solution Approach 2:
The patent performs preliminary data extraction and validation during the compression process itself. By detecting embedded data patterns through correlation analysis on the compressed bit stream and validating them before full decompression, the system prepares and isolates embedded data early in the processing chain, ensuring its integrity is preserved through subsequent operations.
2Measurement precision
If data is embedded in audio at higher amplitude, then data detection accuracy is improved, but impact on cover audio intelligibility increases
Solution Approach 1:
The patent replaces traditional amplitude-based data embedding with a correlation-based detection system operating on compressed audio bits. Instead of mechanically superimposing high-amplitude data signals that disrupt cover audio, the system uses statistical correlation analysis to detect embedded patterns in the compressed bit stream, substituting physical signal manipulation with computational pattern recognition.
Solution Approach 2:
The patent changes the detection parameter from amplitude domain to correlation domain. By measuring the correlation between expected data patterns and the compressed audio bit stream, the system achieves high detection accuracy without requiring high embedding amplitudes, thus avoiding degradation of cover audio intelligibility.
3Adaptability or versatility
If traditional audio steganography techniques are used, then data hiding capability is achieved, but independence from speech codec specifications is lost
Solution Approach 1:
The patent creates a universal data extraction system that operates on the compressed bit stream output of any linear prediction-based speech codec. By designing the correlation-based detection to work with the standardized compressed representation rather than codec-specific internal structures, the system achieves multi-functionality across different speech coding standards while maintaining robust data recovery.
Data Source
AI summary
A method for hiding data within cover audio uses a set of sample codebook waveforms that are each assigned a unique representative digit value. A hidden data sequence representing the data is formed from the waveforms by concatenation of the waveforms assigned to the digit values of the data. The sequence is superimposed upon segments of the cover audio at a fractional amplitude. After transmission, the received signal is decompressed if necessary, the hidden data sequence is recovered from the cover audio, and the data is recovered from the hidden data sequence. This may be done by recovering the locations of the codebook waveforms and interpolating the time markers of the locations. The recovered data may be cleaned up by using estimated distances between successive cross-correlations to discard extraneous correlation peaks and sequence recurrence to probabilistically delete overlapping correlation peaks.