Signal Separation via 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 when dealing with complex digital signals such as audio, video, or mixed data streams.
Innovation Solution
The development of source-agnostic signal processing methods that utilize complex spectral phase evolution (CSPE) to decompose signals into constituent elements, allowing for accurate association and separation of signal components, and recombination of selected portions for enhanced processing or application in systems like speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal separation techniques are used, then signal separation can be performed, but processing time and computational complexity become prohibitively large
Solution Approach 1:
The signal processing is divided into distinct stages: transformation to frequency domain, identification of sinusoidal components, tracking of components across frames, and separation of sources. This segmentation allows each stage to be optimized independently, reducing overall processing time while maintaining separation accuracy.
Solution Approach 2:
The patent performs preliminary transformation of the signal to the frequency domain and identifies sinusoidal components before the actual separation process. By pre-processing the signal in this manner, the subsequent separation operations become more efficient and can be performed in real-time or near real-time.
2Productivity
If conventional signal separation techniques are used, then signal processing can be performed, but real-time or near real-time performance is not achieved
Solution Approach 1:
The patent implements tracking of sinusoidal components across multiple signal frames, using information from previous frames to inform current frame analysis. This feedback mechanism improves identification accuracy while maintaining processing speed, as the tracker refines component estimates iteratively rather than requiring complete re-analysis of each frame.
Solution Approach 2:
The patent replaces traditional time-domain signal processing methods with frequency-domain analysis using Fourier transforms and spectral estimation. This substitution enables more efficient computation and allows for real-time performance while maintaining or improving source identification accuracy through the use of spectral trackers.
Data Source
AI summary
A method includes receiving an input signal comprising an original domain signal and creating a first window data set and a second window data set from the signal, wherein an initiation of the second window data set is offset from an initiation of the first window data set, converting the first window data set and the second window data set to a frequency domain and storing the resulting data as data in a second domain different from the original domain, performing complex spectral phase evolution (CSPE) on the second domain data to estimate component frequencies of the first and second window data sets, using the component frequencies estimated in the CSPE, sampling a set of second-domain high resolution windows to select a mathematical representation comprising a second-domain high resolution window that fits at least one of the amplitude, phase, amplitude modulation and frequency modulation of a component of an underlying signal wherein the component comprises at least one oscillator peak, generating an output signal from the mathematical representation of the original signal as at least one of: an audio file; one or more audio signal components; and one or more speech vectors and outputting the output signal to an external system.