Source Signal Separation Using Cross Channel Complex Spectral Phase Evolution
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Solution Overview
Problem
Conventional signal separation techniques require excessive processing and are often unable to effectively identify and isolate signal sources in real or near real-time, especially in the presence of ambient noise or interfering signals.
Innovation Solution
The development of source-agnostic signal separation methods that utilize complex spectral phase evolution (CSPE) and super-resolution techniques to accurately estimate component frequencies, track oscillator peaks, and reconstruct signals from coherent sources, allowing for efficient separation and recombination of signal components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional signal separation techniques are used, then signal sources can be separated, but processing time and computational complexity become prohibitive
Solution Approach 1:
The patent transforms the signal processing approach by changing parameters from time-domain analysis to frequency-domain analysis using Fourier transforms. This parameter transformation enables real-time processing by converting differential equations into algebraic equations, fundamentally changing how the system processes signals to achieve both accuracy and speed
Solution Approach 2:
The patent replaces conventional mechanical signal separation methods with spectral analysis techniques. Instead of using traditional filtering and separation algorithms, the system uses frequency domain decomposition to identify and separate signal sources, achieving real-time performance without the computational burden of conventional approaches
2Productivity
If conventional signal separation techniques are used, then signal processing can be performed, but the ability to identify and isolate signal sources in real-time is insufficient
Solution Approach 1:
The patent introduces a new dimension of analysis by transforming signals from the time domain to the frequency domain. This dimensional transformation allows the system to analyze signal characteristics that are not apparent in the time domain, enabling both real-time processing and accurate source identification simultaneously through spectral decomposition
3Measurement precision
If signal separation is performed in noisy environments, then individual signal elements can be isolated, but the presence of ambient noise and interfering signals degrades performance
Solution Approach 1:
The patent introduces frequency domain analysis as an intermediary step between raw signal reception and source identification. By transforming signals into the frequency domain, the system creates an intermediate representation where signal sources can be distinguished from noise based on their spectral characteristics, enabling accurate isolation even in noisy environments
Solution Approach 2:
The patent applies localized spectral analysis to identify and extract specific signal components from noisy environments. By analyzing the frequency spectrum and identifying localized peaks corresponding to individual sources, the system can isolate desired signals while filtering out ambient noise and interference that occupy different frequency regions
Data Source
AI summary
A method of processing a signal includes taking a signal recorded by a plurality of signal recorders, applying at least one super-resolution technique to the signal to produce an oscillator peak representation of the signal comprising a plurality of frequency components for a plurality of oscillator peaks, computing at least one Cross Channel Complex Spectral Phase Evolution (XCSPE) attribute for the signal to produce a measure of a spatial evolution of the plurality of oscillator peaks between the signal, identifying a known predicted XCSPE curve (PXC) trace corresponding to the frequency components and at least one XCSPE attribute of the plurality of oscillator peaks and utilizing the identified PXC trace to determine a spatial attribute corresponding to an origin of the signal.