Frequency Domain Voice Authentication Signal Processing
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Solution Overview
Problem
Existing voice authentication systems face challenges in efficiently processing and authenticating voices due to high computational resource requirements and the difficulty in distinguishing voices under varying volumes and intonations, especially in constrained environments like mobile devices.
Innovation Solution
The system generates a voice identification (ID) by converting the voice signal into the frequency or time domain, setting predetermined amplitudes and frequencies, applying filters, and digitizing harmonic components to create a concise digital representation that can authenticate voices across different volumes and intonations, reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional voice authentication methods are used, then voice authentication can be performed, but computational resource requirements are high
Solution Approach 1:
The patent extracts only the essential voice characteristics (amplitude and frequency of harmonic components) from the complete voice signal, discarding redundant information. This extraction approach maintains authentication accuracy while significantly reducing computational resource requirements by processing only the most relevant features.
Solution Approach 2:
The patent creates a simplified digital representation (copy) of the voice signal in the frequency domain, which captures the essential characteristics needed for authentication without requiring processing of the original complex time-domain signal. This digital model enables efficient comparison and authentication with reduced computational load.
2Measurement precision
If detailed voice characteristics are extracted, then authentication accuracy is improved, but data representation size increases
Solution Approach 1:
The patent selectively extracts only the amplitude and frequency parameters of harmonic components from the voice signal, omitting other detailed characteristics. This selective extraction maintains sufficient authentication precision while minimizing the quantity of data that must be stored and processed.
Solution Approach 2:
The patent transforms the voice signal from the time domain to the frequency domain, changing the representation parameters from temporal waveforms to spectral components. This parameter transformation enables compact representation of voice characteristics through amplitude and frequency values of harmonics, reducing data size while preserving authentication accuracy.
3Ease of operation
If voice authentication is performed in time domain, then processing is straightforward, but it is difficult to distinguish voices under varying volumes and intonations
Solution Approach 1:
The patent transitions the voice analysis from the time domain to the frequency domain, adding a spectral dimension to the analysis. This dimensional change enables the system to capture amplitude and frequency characteristics of harmonic components, providing robustness against volume and intonation variations while maintaining processing simplicity through systematic frequency domain operations.
Data Source
AI summary
A system and method are provided to authenticate a voice in a frequency domain. A voice in the time domain is transformed to a signal in the frequency domain. The first harmonic is set to a predetermined frequency and the other harmonic components are equalized. Similarly, the amplitude of the first harmonic is set to a predetermined amplitude, and the harmonic components are also equalized. The voice signal is then filtered. The amplitudes of each of the harmonic components are then digitized into bits to form at least part of a voice ID. In another system and method, a voice is authenticated in a time domain. The initial rise time, initial fall time, second rise time, second fall time and final oscillation time are digitized into bits to form at least part of a voice ID. The voice IDs are used to authenticate a user's voice.