Loudspeaker Preference Prediction Model Using Regression Analysis
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Solution Overview
Problem
Current models for predicting loudspeaker preferences based on objective measurements are inadequate due to disagreements on measurement environments and types, low frequency resolution, and failure to account for psychoacoustic effects, leading to inaccurate and non-generalizable predictions.
Innovation Solution
A statistical regression model that correlates loudspeaker preference ratings with spatially averaged frequency response deviations measured at least 1/7th octaves, using a comprehensive set of independent variables such as absolute average deviation, narrow band deviation, smoothness, and low frequency extension, to predict listener preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If listening tests are performed to accurately predict loudspeaker preferences, then prediction accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates a predictive model that copies the results of expensive listening tests by using inexpensive objective measurements. The model is trained on data from listening tests and then uses objective measurements (frequency response, impedance, etc.) to predict preferences without requiring actual listening tests, thus copying the effect of subjective evaluation through objective means.
Solution Approach 2:
The patent replaces the mechanical system of human listening tests with an automated computational model. Instead of requiring human listeners to physically evaluate loudspeakers (mechanical human involvement), the system uses automated measurements and regression analysis to predict preferences, substituting human sensory evaluation with computational prediction.
2Ease of manufacture
If low-resolution 1/3-octave measurements are used, then measurement simplicity is improved, but ability to distinguish resonances deteriorates
Solution Approach 1:
The patent segments the frequency spectrum into finer intervals than traditional 1/3-octave bands, using 1/12-octave or even 1/24-octave bands. This segmentation allows the model to resolve individual resonances and spectral details that would be smoothed over in coarser measurements, while still maintaining a structured approach to frequency analysis.
Solution Approach 2:
The patent changes the measurement parameter from coarse 1/3-octave bandwidth to finer 1/12-octave or 1/24-octave bandwidth. This parameter change increases the resolution of frequency measurements, allowing the model to detect and account for individual resonances and spectral characteristics that affect listener preference but would be invisible at lower resolution.
3Measurement precision
If comprehensive anechoic measurements are used, then measurement accuracy is improved, but generalizability to real listening environments deteriorates
Solution Approach 1:
The patent incorporates room-specific acoustic characteristics into the predictive model, allowing the model to adapt to local listening environments. Rather than using a single universal model, the system can be tailored to specific rooms by measuring and incorporating their acoustic properties (reverberation time, mode frequencies, etc.), thus achieving both measurement accuracy and environmental generalizability.
Solution Approach 2:
The patent performs preliminary measurements of the listening room's acoustic characteristics before conducting loudspeaker evaluations. By measuring room modes, reverberation times, and other acoustic properties in advance, the model can compensate for room-specific effects and generalize predictions across different listening conditions while maintaining accuracy for the specific environment.
4Device complexity
If psychoacoustic effects are excluded from the model, then model simplicity is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces psychoacoustic parameters as intermediary variables that bridge the gap between physical measurements and subjective perception. Instead of directly modeling complex human auditory processing, the system uses intermediate psychoacoustic metrics (loudness, sharpness, roughness, etc.) that are derived from objective measurements but account for perceptual effects, thereby improving accuracy without requiring full complexity of human audition.
Data Source
AI summary
A general model is provided for predicting a loudspeaker preference rating, where the model's predicted loudspeaker preference rating is calculated based upon the sum of a plurality of weighted independent variables that statistically quantify amplitude deviations in a loudspeaker frequency response. The independent variables selected may be independent variables determined as maximizing the ability of a loudspeaker preference variable to predict a loudspeaker preference rating. A multiple regression analysis is performed to determine respective weights for the selected independent variables. The weighted independent variables are arranged into a linear relationship on which the loudspeaker preference variable depends.