Audio Bio-marker Obfuscation via Conditional GAN
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Solution Overview
Problem
Digital representations of human voices contain sensitive information about health conditions that individuals may not wish to disclose, and existing audio processing systems lack mechanisms to control or obfuscate this information effectively.
Innovation Solution
A method involving a supervised discriminator, a denoising autoencoder, and a conditional auxiliary generative adversarial network (GAN) is deployed in an audio processing system to obfuscate bio-markers in audio samples, maintaining the quality and fidelity of the audio while hiding features that could be used to infer health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio samples are processed to remove bio-markers for privacy protection, then health privacy is improved, but audio quality and fidelity deteriorate
Solution Approach 1:
The patent introduces a generative adversarial network (GAN) as an intermediary system between the audio input and the final obfuscated output. The GAN consists of a generator that creates obfuscated audio by removing bio-markers and a discriminator that evaluates the authenticity of the generated audio. This intermediary system enables privacy protection while maintaining audio quality, as the discriminator ensures the output sounds natural and preserves speech intelligibility even after bio-marker removal.
Solution Approach 2:
The patent transforms the audio processing approach by changing parameters in the frequency domain rather than time domain. The system converts audio signals to frequency representations, applies bio-marker removal operations on spectral features, and then reconstructs the audio. This parameter transformation allows selective modification of specific frequency components associated with bio-markers while preserving other audio characteristics that contribute to quality and intelligibility.
2Manufacturing precision
If traditional audio processing methods are used, then audio quality is maintained, but health information privacy is compromised
Solution Approach 1:
The patent applies the extraction principle by specifically isolating and removing bio-marker information from the audio signal. The system identifies frequency components and temporal patterns that carry health-related information, extracts these specific features, and removes them while leaving the rest of the audio intact. This selective extraction enables privacy protection without affecting overall audio quality, as only the problematic health-indicative features are removed.
Solution Approach 2:
The patent creates a synthetic copy of the audio signal through the GAN generator that mimics the original audio's statistical properties and perceptual characteristics without containing the original bio-markers. The generator produces a realistic-sounding audio output that preserves speech intelligibility and naturalness, effectively creating a privacy-protected replica that maintains audio quality while eliminating health information.
3Object-affected harmful factors
If bio-markers are removed from audio samples, then privacy security is enhanced, but speech intelligibility may be affected
Solution Approach 1:
The patent implements feedback through the discriminator component of the GAN system. The discriminator evaluates the generated obfuscated audio and provides feedback to the generator about whether the output maintains speech intelligibility and naturalness. This feedback loop allows the system to iteratively improve the obfuscation process, ensuring that bio-markers are removed while preserving the essential speech characteristics that enable intelligibility. The discriminator acts as a quality control mechanism that prevents degradation of speech understanding.
Data Source
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AI summary
A supervised discriminator for detecting bio-markers in an audio sample dataset is trained and a denoising autoencoder is trained to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset. A conditional auxiliary generative adversarial network (GAN) trained to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers. The conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in an audio processing system