Auralization Sameness Evaluation Model for Acoustic Signals
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Solution Overview
Problem
Current methods for evaluating the quality of auralization of synthetic acoustic signals lack a deterministic and objective criterion, relying on subjective human perception or incomplete objective measures that fail to quantify the accuracy and realism of sound propagation and emission in complex environments.
Innovation Solution
A method using a machine learning model trained on human perception data to evaluate the sameness between synthesized and reference acoustic signals, providing a deterministic quality criterion through objective single-number values, by simulating and modifying sound pressure and phase parameters across frequency ranges to align with reference signals, facilitating automated evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective human perception methods are used to evaluate auralization quality, then the evaluation reflects human auditory experience, but the evaluation lacks objectivity and determinism
Solution Approach 1:
The patent replaces subjective human perception (mechanical/physical system) with an automated evaluation system based on signal processing and algorithms (information processing system). The objective evaluation method uses computational approaches to assess auralization quality without requiring human listeners, thereby achieving determinism and objectivity while reducing system complexity related to human subjectivity.
Solution Approach 2:
The patent introduces an intermediate evaluation system that acts as a mediator between the auralization output and the final quality assessment. This intermediary system processes the synthesized and reference signals through objective criteria and algorithms, providing a deterministic bridge that eliminates the need for direct human perception while maintaining evaluation accuracy.
2Measurement precision
If traditional objective measurement methods are used, then the evaluation is automated and objective, but the evaluation fails to capture human perception accuracy
Solution Approach 1:
The patent changes the evaluation parameters from traditional objective physical measurements to perceptually-relevant parameters that align with human auditory perception. By transforming the measurement space to match human perception characteristics, the system achieves both perceptual accuracy and automated efficiency, as the evaluation criteria are designed to reflect how humans actually perceive sound quality.
Solution Approach 2:
The patent segments the evaluation process into distinct components: reference signal acquisition, synthesis signal generation, and objective comparison. This segmentation allows each component to be optimized independently, with the comparison stage specifically designed to match human perceptual thresholds, thereby achieving high perception accuracy through structured automated processing.
3Measurement precision
If comprehensive signal analysis is performed to quantify auralization quality, then the evaluation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing the comprehensive signal analysis only on the specific parameters that most significantly impact auralization quality. Rather than analyzing all possible signal characteristics, the method selectively evaluates the most perceptually relevant features, achieving high quality quantification accuracy while reducing unnecessary computational complexity.
Data Source
AI summary
A method for evaluating the sameness of an auralization. The method including: providing a synthesis signal to map a real acoustic signal of a device; providing a reference signal that results from a reference measurement of a real acoustic signal of a device; evaluating the sameness between the provided synthesis signal and the provided reference signal to obtain a sameness result for the auralization, wherein the evaluation is carried out on the basis of an evaluation model for sameness according to human perception; providing the sameness result; wherein the evaluation of the sameness is carried out for at least two modifications of the provided synthesis signal in order to separately evaluate at least one frequency component of the synthesis signal.