Audio Watermark Encoding Using Self-Correlation for Reverberation
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Solution Overview
Problem
Existing audio watermarking technologies face challenges in detecting watermarks in audio data due to reverberation effects, leading to inaccurate detection and inefficient processing, especially when watermarked audio is output by loudspeakers and recaptured by microphones.
Innovation Solution
The system employs a self-correlation algorithm and bi-layer watermark encoding structure using eigenvectors and sign sequences to cancel repetitive portions of the original audio segment, enabling accurate detection of audio watermarks despite reverberation, and allows for embedding multiple watermarks in audio data for parallel decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio watermarking is performed on audio data that will be output by loudspeakers and recaptured by microphones, then the system can enable unique functionality between devices, but reverberation effects cause inaccurate detection of watermarks
Solution Approach 1:
The system performs preliminary actions by embedding watermarks in advance and using self-correlation algorithms to anticipate and compensate for reverberation effects before they interfere with detection accuracy
Solution Approach 2:
The system uses feedback mechanisms where the self-correlation algorithm processes the watermarked audio signal to generate corrections that compensate for reverberation, continuously adjusting the detection process to maintain accuracy despite acoustic distortions
2Ease of manufacture
If existing audio watermarking technologies are used, then the process is simple, but detection becomes inefficient and inaccurate due to reverberation
Solution Approach 1:
The self-correlation algorithm performs self-service by processing the watermarked audio signal itself to extract and correct reverberation effects, eliminating the need for complex external processing systems while improving detection efficiency
3Productivity
If multiple watermarks are embedded in audio data for parallel decoding, then processing complexity is reduced, but the encoding structure becomes more complex
Solution Approach 1:
The system segments the watermark embedding process into parallel components, allowing multiple watermarks to be encoded and processed simultaneously, which reduces overall processing complexity while maintaining manageable encoding structure through modular design
Data Source
AI summary
A system may embed audio watermarks in audio data using an Eigenvector matrix. The system may detect audio watermarks in audio data despite the effects of reverberation. For example, the system may embed multiple repetitions of an audio watermark before generating output audio using loudspeaker(s). To detect the audio watermark in audio data generated by a microphone, the system may perform a self-correlation that indicates where the audio watermark is repeated. In some examples, the system may encode the audio watermark using multiple repetitions of a multi-segment Eigenvector. Additionally or alternatively, the system may encode the audio watermark using a binary sequence of positive and negative values, which may be used as a shared key for encoding/decoding the audio watermark. The audio watermark can be embedded in output audio data to enable wakeword suppression (e.g., avoid cross-talk between devices) and/or local signal transmission between devices in proximity to each other.