Audio Frequency Response Prediction From Evaluator Scoring
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Solution Overview
Problem
Existing methods for determining the frequency response of audio systems, such as car audio systems, rely heavily on human expert evaluations, which are subjective and time-consuming, and lack efficient predictive models to accurately assess sound quality based on measurable frequency responses.
Innovation Solution
A computer-implemented method using Generative Adversarial Networks (GANs) to predict frequency responses of audio systems by training a GAN discriminator on evaluator scorings and a GAN generator on audio system settings and environments, allowing for objective and efficient prediction of sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expert evaluations are used to determine frequency response, then sound quality assessment is performed, but the process is subjective and time-consuming
Solution Approach 1:
The patent creates a virtual copy of the human expert evaluation process through a neural network model trained on expert scorings. The GAN generator learns to predict frequency responses that match expert evaluations, effectively copying the expert's subjective assessment capability into an automated computational model, thereby eliminating time-consuming manual evaluations while preserving assessment accuracy
Solution Approach 2:
The patent replaces the mechanical process of human expert listening and scoring with an automated computational system. The neural network-based GAN model substitutes the human expert's sensory and cognitive processing with algorithmic prediction, transforming a subjective human task into an objective computational process that delivers consistent results without time constraints
2Measurement precision
If measured frequency response is used to characterize audio system, then objective data is obtained, but predictive models for sound quality are lacking
Solution Approach 1:
The patent implements a feedback mechanism where the GAN generator's predictions are continuously refined based on comparison with actual expert evaluations. The model learns from the discrepancy between predicted and actual sound quality assessments, adjusting its internal parameters to improve prediction accuracy. This feedback loop bridges the gap between objective frequency response data and subjective sound quality perception
Solution Approach 2:
The patent creates a composite predictive model that combines multiple elements: measured frequency response data, expert evaluation scorings, and neural network learned relationships. This composite approach integrates objective measurements with subjective assessment patterns, producing a hybrid model that captures both the physical properties of the audio system and the perceptual aspects of sound quality
Data Source
AI summary
A computer-implemented method for determining a frequency response of an audio system, the method comprising: training a Generative Adversarial Network, GAN, discriminator on a first training dataset comprising measured frequency responses of reference audio systems to a test signal and an evaluator scoring of the audio system to predict a predicted scoring for the reference audio systems, training a GAN generator on a second training dataset comprising evaluator scorings to predict a predicted frequency response for the reference audio systems, wherein training the GAN generator comprises processing the predicted frequency response by the trained GAN discriminator to predict a predicted scoring; and processing a production dataset comprising an input scoring of a production audio system by the trained GAN generator to predict a frequency response of the production audio system.