Stereo Ambience Extraction Using Least-Squares Crosstalk Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current stereo upmixing techniques struggle to effectively extract ambience components from multi-channel signals while maintaining phase relationships and reducing cross-correlation, leading to processing artifacts and compromised listening experiences.
Innovation Solution
The method involves converting a multi-channel input signal into a time-frequency representation, computing cross-correlation and autocorrelation coefficients, and applying crosstalk and same-side coefficients as a function of a tuning parameter to extract ambience components, thereby controlling crosstalk and maintaining phase relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional stereo upmixing techniques are used to extract ambience components, then the extraction process is simplified, but cross-correlation between channels increases and phase relationships are compromised
Solution Approach 1:
The patent transforms the stereo signal into the time-frequency domain using Short-Time Fourier Transform (STFT), changing the representation parameters from time-domain to time-frequency domain. This transformation enables independent manipulation of magnitude and phase spectra, allowing for accurate ambience extraction while preserving phase relationships. The frequency-domain processing facilitates controlled manipulation of cross-correlation properties without compromising temporal phase information.
Solution Approach 2:
The patent introduces an intermediary processing stage in the time-frequency domain that acts as a mediator between the input stereo signal and the output multichannel signal. By computing ambience components through intermediate calculations involving cross-correlation coefficients and energy normalization, the system achieves accurate ambience separation while maintaining phase coherence. This intermediary representation allows for controlled extraction without direct time-domain manipulation that would compromise phase relationships.
2Productivity
If conventional ambience extraction methods are used, then processing speed is maintained, but processing artifacts increase and listening experience deteriorates
Solution Approach 1:
The patent changes the processing parameters by working in the frequency domain where ambience components can be identified and extracted based on their spectral characteristics. By analyzing the magnitude spectrum and computing cross-correlation coefficients in the frequency domain, the system can distinguish ambience from direct sound more effectively, reducing processing artifacts while maintaining efficient computation through Fast Fourier Transform algorithms.
Solution Approach 2:
The patent replaces traditional time-domain signal processing mechanisms with frequency-domain processing. Instead of using time-domain filtering and mixing operations that generate artifacts, the system uses frequency-domain spectral analysis and complex number arithmetic to extract and redistribute ambience components. This substitution of processing domain eliminates many artifacts inherent in time-domain approaches while maintaining computational efficiency.
3Measurement precision
If cross-correlation reduction is prioritized in ambience extraction, then channel separation improves, but phase relationships are lost and sound quality deteriorates
Solution Approach 1:
The patent segments the stereo signal into distinct components (direct sound and ambience) in the time-frequency domain by analyzing spectral characteristics. By computing cross-correlation coefficients and comparing them against threshold values, the system separates correlated direct sound from uncorrelated ambience components. This segmentation occurs in the frequency domain where phase information is preserved, allowing subsequent processing to maintain both channel separation and phase relationships.
Solution Approach 2:
The patent moves the processing from the time domain to the time-frequency domain, adding a frequency dimension to the analysis. This dimensional transformation allows for independent control of magnitude and phase characteristics. By operating in this extended domain, the system can achieve precise channel separation through frequency-selective processing while preserving temporal phase relationships, as the phase information is explicitly maintained in the complex frequency-domain representation.
Data Source
AI summary
Ambience extraction from a multichannel input signal is provided. The multichannel input signal is converted into a time-frequency representation. A cross-correlation coefficient is computed for each time and frequency in the time-frequency representation of the multichannel input signal. An autocorrelation is computed for each time and frequency in the time-frequency representation of the multichannel input signal. Using the cross-correlation coefficient and the autocorrelation, ambience extraction coefficients including crosstalk and same-side coefficients are computed as a function of a tuning parameter, the crosstalk coefficients being proportional to the tuning parameter and the tuning parameter being between a value of 0 and a value of 1. The ambience extraction coefficients are applied to extract a left ambience component and a right ambience component.