Speech Signal Anonymization via Frequency Domain Modulation
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Solution Overview
Problem
Conventional signal processing techniques for modifying digital speech data to avoid feature extraction, such as anonymizing gender, pitch, and cadence, require substantial processing resources and time, and are inefficient in preventing feature extraction.
Innovation Solution
The techniques involve receiving a modulated signal comprising a speech signal and a carrier wave, converting them into spectral signals, determining spectral bands, calculating weighted spectral band values, and modifying the carrier wave with these values to generate a modified spectral signal that is then transmitted, effectively anonymizing the speech signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional signal processing techniques (wavelet techniques, convolution procedures) are used to modify digital speech data, then the speech data can be modified to avoid feature extraction, but substantial processing resources and time expenditure are required
Solution Approach 1:
The patent applies parameter changes by transforming the speech signal from the time domain to the frequency domain using Fast Fourier Transform (FFT), then modifying spectral parameters (amplitude, phase, frequency) of individual frequency components. This allows efficient anonymization by directly manipulating frequency-domain parameters rather than using complex time-domain convolution procedures, thereby reducing processing resources while maintaining feature extraction prevention.
Solution Approach 2:
The patent segments the speech signal into distinct spectral bands (e.g., fundamental frequency components and harmonic components) using frequency domain analysis. By processing each spectral band independently through targeted modifications (adjusting amplitude of specific frequency components), the system achieves efficient feature anonymization without requiring exhaustive processing of the entire signal, thus improving productivity while maintaining reliability.
2Reliability
If conventional signal processing techniques are used to anonymize speech data, then gender, pitch, and cadence features can be modified, but significant processing resources are consumed
Solution Approach 1:
The patent replaces complex mechanical signal processing operations (convolution procedures, wavelet transformations) with more efficient frequency-domain operations. By using Fast Fourier Transform to convert the signal to the frequency domain and then applying simple amplitude and phase adjustments to individual frequency components, the system achieves effective anonymization with significantly reduced processing resource consumption and energy expenditure.
Solution Approach 2:
The patent efficiently anonymizes speech by directly modifying frequency-domain parameters (amplitude, phase, frequency) of spectral components. This parameter-based approach allows targeted adjustment of pitch, cadence, and other identifying features through simple mathematical operations on frequency components, rather than requiring resource-intensive time-domain processing, thus reducing energy consumption while maintaining anonymization effectiveness.
3Reliability
If conventional modulation techniques are used to modify digital speech data, then feature extraction can be avoided, but the processing time expenditure is substantial
Solution Approach 1:
The patent segments the speech signal into discrete frequency components using Fast Fourier Transform, then processes each component independently through simple amplitude and phase modifications. This segmentation approach enables parallel processing of multiple frequency components simultaneously, significantly reducing total processing time compared to sequential time-domain convolution operations, while maintaining effective feature extraction prevention.
Solution Approach 2:
The patent substitutes complex time-domain modulation operations with efficient frequency-domain transformations. By using Fast Fourier Transform to convert the signal and then applying simple complex number multiplication for modulation, the system achieves feature anonymization with substantially reduced processing time, as frequency-domain operations are computationally more efficient than repeated time-domain convolutions.
Data Source
AI summary
Systems and methods are disclosed herein for modifying modulated signals for transmission. The system receives a modulated signal comprising a speech signal and a carrier wave and generates first and second spectral signals by converting the modulation signal and carrier wave from the time domain to the frequency domain respectively. The system then determines spectral bands for the first and second spectral signals. For each spectral band, the system calculates a weighted spectral band value based on a magnitude of the first spectral signal within the spectral band and generates a modified spectral signal by modifying the second spectral signal with the weighted spectral band value. The system then converts the modified spectral signal from the frequency domain to the time domain and transmits the converted modified spectral signal to a server.